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    <description>recent bookmarks from cshalizi</description>
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	<rdf:li rdf:resource="https://www.theverge.com/2024/12/5/24313222/chatgpt-pardon-biden-bush-esquire"/>
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	<rdf:li rdf:resource="https://www.povertyactionlab.org/evaluations"/>
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	<rdf:li rdf:resource="https://arxiv.org/abs/1311.5828"/>
	<rdf:li rdf:resource="http://www.economist.com/news/united-states/21710265-local-health-outcomes-predict-trumpward-swings-illness-indicator?fsrc=scn/tw_ec/illness_as_indicator"/>
	<rdf:li rdf:resource="http://www.talyarkoni.org/blog/2016/06/11/the-great-minds-journal-club-discusses-westfall-yarkoni-2016/"/>
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	<rdf:li rdf:resource="https://github.com/Quartz/bad-data-guide"/>
	<rdf:li rdf:resource="http://notstatschat.tumblr.com/post/64556449200/barren-proxies"/>
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  </channel><item rdf:about="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5152665">
    <title>The Sources of Researcher Variation in Economics by Nick Huntington-Klein, Claus C. Pörtner, Yubraj Acharya, Matus Adamkovic, Joop Adema, Lameck Ondieki Agasa, Imtiaz Ahmad, Mevlude Akbulut-Yuksel, Martin Eckhoff Andresen, David Angenendt, José-Ignacio </title>
    <dc:date>2025-09-05T16:16:09+00:00</dc:date>
    <link>https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5152665</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We use a rigorous three-stage many-analysts design to assess how different researcher decisions—specifically data cleaning, research design, and the interpretation of a policy question—affect the variation in estimated treatment effects. A total of 146 research teams each completed the same causal inference task three times each: first with few constraints, then using a shared research design, and finally with pre-cleaned data in addition to a specified design. We find that even when analyzing the same data, teams reach different conclusions. In the first stage, the interquartile range (IQR) of the reported policy effect was 3.1 percentage points, with substantial outliers. Surprisingly, the second stage, which restricted research design choices, exhibited slightly higher IQR (4.0 percentage points), largely attributable to imperfect adherence to the prescribed protocol. By contrast, the final stage, featuring standardized data cleaning, narrowed variation in estimated effects, achieving an IQR of 2.4 percentage points. Reported sample sizes also displayed significant convergence under more restrictive conditions, with the IQR dropping from 295,187 in the first stage to 29,144 in the second, and effectively zero by the third. Our findings underscore the critical importance of data cleaning in shaping applied microeconomic results and highlight avenues for future replication efforts."]]></description>
<dc:subject>to:NB social_science_methodology statistics to_teach:undergrad-ADA to_teach:undergrad-research</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:23ac152addad/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_science_methodology"/>
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<item rdf:about="https://openreview.net/forum?id=SBE2q9qwZj">
    <title>Fast Computation of Leave-One-Out Cross-Validation for $k$-NN Regression | OpenReview</title>
    <dc:date>2025-09-02T19:30:09+00:00</dc:date>
    <link>https://openreview.net/forum?id=SBE2q9qwZj</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We describe a fast computation method for leave-one-out cross-validation (LOOCV) for 
$k$-nearest neighbours ($k$-NN) regression. We show that, under a tie-breaking condition for nearest neighbours, the LOOCV estimate of the mean square error for $k$
-NN regression is identical to the mean square error of $(k+1)$-NN regression evaluated on the training data, multiplied by the scaling factor $(k+1)^2/𝑘^2$. Therefore, to compute the LOOCV score, one only needs to fit $(k+1)$-NN regression only once, and does not need to repeat training-validation of $k$-NN regression for the number of training data. Numerical experiments confirm the validity of the fast computation method."

!!!]]></description>
<dc:subject>to:NB to_read nearest_neighbors to_teach:data-mining to_teach:undergrad-ADA via:?</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a7ddd207a4f6/</dc:identifier>
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<item rdf:about="https://www.cmu.edu/news/stories/archives/2025/may/alumnus-nick-thieme-credits-cmu-in-pulitzer-prize-win">
    <title>Alumnus Nick Thieme Credits CMU in Pulitzer Prize Win - News - Carnegie Mellon University</title>
    <dc:date>2025-05-08T17:56:54+00:00</dc:date>
    <link>https://www.cmu.edu/news/stories/archives/2025/may/alumnus-nick-thieme-credits-cmu-in-pulitzer-prize-win</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>kith_and_kin to_teach:undergrad-ADA thieme.nicholas additive_models journalism</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:930f5a4357b9/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:thieme.nicholas"/>
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<item rdf:about="https://elevanth.org/blog/2023/07/17/none-of-the-above/">
    <title>None of the Above | Elements of Evolutionary Anthropology</title>
    <dc:date>2025-03-23T17:17:45+00:00</dc:date>
    <link>https://elevanth.org/blog/2023/07/17/none-of-the-above/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[--- If I am honest with myself, incorporating something like this (or even my own paper with Gelman!) into undergrad ADA would require a big re-design of the course, because it's currently "here is an array of sometimes-useful statistical methods", not "here is how you turn scientific questions into data-analytic problems, and statistical solutions back into scientific answers".  Knowing a lot of methods is _helpful_ to that undertaking, but it's different.  Maybe that's too much to ask of an undergrad class with >200 students/year...]]></description>
<dc:subject>statistics data_analysis have_read mcelreath.richard closing_old_tabs re:phil-of-bayes_paper to_teach:undergrad-ADA</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:1079612a0b01/</dc:identifier>
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<item rdf:about="https://www.cambridge.org/core/journals/journal-of-economic-history/article/we-do-not-know-the-population-of-every-country-in-the-world-for-the-past-two-thousand-years/D747DDC6E499C799B0471DBE33FEB0BB">
    <title>We Do Not Know the Population of Every Country in the World for the Past Two Thousand Years | The Journal of Economic History | Cambridge Core</title>
    <dc:date>2025-03-10T14:07:54+00:00</dc:date>
    <link>https://www.cambridge.org/core/journals/journal-of-economic-history/article/we-do-not-know-the-population-of-every-country-in-the-world-for-the-past-two-thousand-years/D747DDC6E499C799B0471DBE33FEB0BB</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Economists have reported results based on populations for every country in the world for the past two thousand years. The source, McEvedy and Jones’ Atlas of World Population History, includes many estimates that are little more than guesses and that do not reflect research since 1978. McEvedy and Jones often infer population sizes from their view of a particular economy, making their estimates poor proxies for economic growth. Their rounding means their measurement error is not “classical.” Some economists augment that error by disaggregating regions in unfounded ways. Econometric results that rest on McEvedy and Jones are unreliable.
"“… we haven’t just pulled the figures out of the sky. Well, not often.”
"—McEvedy and Jones (1978, p. 11)"

--- I want to teach this to The Kids, but it simultaneously expects too much historical knowledge on their part, and would make too many of them nihilists about social science.]]></description>
<dc:subject>to:NB have_read history economic_history econometrics social_measurement bad_data_collection demography bad_data_analysis to_teach:undergrad-ADA tab_closure</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b68c7ada7248/</dc:identifier>
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<item rdf:about="https://www.theverge.com/2024/12/5/24313222/chatgpt-pardon-biden-bush-esquire">
    <title>Stop using generative AI as a search engine - The Verge</title>
    <dc:date>2024-12-06T13:56:06+00:00</dc:date>
    <link>https://www.theverge.com/2024/12/5/24313222/chatgpt-pardon-biden-bush-esquire</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[--- The "to_teach" tags are just the courses I'm gearing up for in the next few semesters; I suspect I will be giving _this_ lesson (and having it ignored) for many years to come.]]></description>
<dc:subject>large_language_models_(so_called) to_teach to_teach:data-mining to_teach:undergrad-ADA to_teach:statistics_of_inequality_and_discrimination information_retrieval have_read via:?</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:4dc951b569f7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:large_language_models_(so_called)"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data-mining"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:statistics_of_inequality_and_discrimination"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:information_retrieval"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
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<item rdf:about="https://academic.oup.com/qje/article-abstract/139/2/891/7473710">
    <title>Logs with Zeros? Some Problems and Solutions* | The Quarterly Journal of Economics | Oxford Academic</title>
    <dc:date>2024-06-24T13:38:04+00:00</dc:date>
    <link>https://academic.oup.com/qje/article-abstract/139/2/891/7473710</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["When studying an outcome Y that is weakly positive but can equal zero (e.g., earnings), researchers frequently estimate an average treatment effect (ATE) for a “log-like” transformation that behaves like log (Y) for large Y but is defined at zero (e.g., log (1 + Y), arcsinh(𝑌)⁠). We argue that ATEs for log-like transformations should not be interpreted as approximating percentage effects, since unlike a percentage, they depend on the units of the outcome. In fact, we show that if the treatment affects the extensive margin, one can obtain a treatment effect of any magnitude simply by rescaling the units of Y before taking the log-like transformation. This arbitrary unit dependence arises because an individual-level percentage effect is not well-defined for individuals whose outcome changes from zero to nonzero when receiving treatment, and the units of the outcome implicitly determine how much weight the ATE for a log-like transformation places on the extensive margin. We further establish a trilemma: when the outcome can equal zero, there is no treatment effect parameter that is an average of individual-level treatment effects, unit invariant, and point identified. We discuss several alternative approaches that may be sensible in settings with an intensive and extensive margin, including (i) expressing the ATE in levels as a percentage (e.g., using Poisson regression), (ii) explicitly calibrating the value placed on the intensive and extensive margins, and (iii) estimating separate effects for the two margins (e.g., using Lee bounds). We illustrate these approaches in three empirical applications."]]></description>
<dc:subject>to:NB regression causal_inference to_teach:undergrad-ADA</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:67420daed294/</dc:identifier>
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<item rdf:about="https://onlinelibrary.wiley.com/doi/abs/10.1111/1745-9125.12353#crim12353-supitem-0001">
    <title>Streetwork at the crossroads: An evaluation of a street gang outreach intervention and holistic appraisal of the research evidence - Hureau - 2023 - Criminology - Wiley Online Library</title>
    <dc:date>2024-03-04T14:27:53+00:00</dc:date>
    <link>https://onlinelibrary.wiley.com/doi/abs/10.1111/1745-9125.12353#crim12353-supitem-0001</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Spurred by the success of public health violence interventions, and accelerated by policy pressure to reduce violence without exacerbating overpolicing and mass incarceration, streetwork programs—those that provide anti-violence services by neighborhood-based workers who perform their work beyond the walls of parochial institutions—have positioned themselves as the most important non–law-enforcement violence prevention option available to urban policy makers. Yet despite their importance, the state of the field seems difficult to interpret for academics and practitioners alike. In this article, we make several contributions that bring forth new findings and deliver new perspectives on streetwork as a violence reduction strategy. First, we offer an extended analytic review of the streetwork evaluation literature that connects the study of contemporary public health violence interventions to a preceding tradition of criminologically inspired streetwork studies. Second, we present the results of an impact evaluation of StreetSafe Boston (SSB)—a multiyear streetwork intervention that served 20 Boston gangs. We find that the SSB intervention had no detectable effect on violence among the gangs that it served. We conclude by offering a framework for understanding a field at multiple crossroads: past and present, proclaimed successes and failures, help and harm."

--- That last sentence is the most poetic way I can remember of announcing an informative null result.
--- Last tag is conditional on finding replication data.]]></description>
<dc:subject>to:NB to_read causal_inference crime violence public_policy winship.christopher to_teach:undergrad-ADA</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f109c63b3182/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:crime"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:violence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:public_policy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:winship.christopher"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0286067">
    <title>Causal implicatures from correlational statements | PLOS ONE</title>
    <dc:date>2023-05-22T18:20:47+00:00</dc:date>
    <link>https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0286067</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Correlation does not imply causation, but this does not necessarily stop people from drawing causal inferences from correlational statements. We show that people do in fact infer causality from statements of association, under minimal conditions. In Study 1, participants interpreted statements of the form “X is associated with Y” to imply that Y causes X. In Studies 2 and 3, participants interpreted statements of the form “X is associated with an increased risk of Y” to imply that X causes Y. Thus, even the most orthodox correlational language can give rise to causal inferences."

--- Good to have this confirmed!]]></description>
<dc:subject>causal_inference psychology to_teach:undergrad-ADA gershman.samuel via:? in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:86677951c9ee/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:psychology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:gershman.samuel"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://jakehofman.com/publication/visualizing-inferential-uncertainty/">
    <title>How visualizing inferential uncertainty can mislead readers about treatment effects in scientific results 🏆 | jakehofman.com</title>
    <dc:date>2023-02-13T14:57:39+00:00</dc:date>
    <link>http://jakehofman.com/publication/visualizing-inferential-uncertainty/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["When presenting visualizations of experimental results, scientists often choose to display either inferential uncertainty (e.g., uncertainty in the estimate of a population mean) or outcome uncertainty (e.g., variation of outcomes around that mean) about their estimates. How does this choice impact readers’ beliefs about the size of treatment effects? We investigate this question in two experiments comparing 95% confidence intervals (means and standard errors) to 95% prediction intervals (means and standard deviations). The first experiment finds that participants are willing to pay more for and overestimate the effect of a treatment when shown confidence intervals relative to prediction intervals. The second experiment evaluates how alternative visualizations compare to standard visualizations for different effect sizes. We find that axis rescaling reduces error, but not as well as prediction intervals or animated hypothetical outcome plots (HOPs), and that depicting inferential uncertainty causes participants to underestimate variability in individual outcomes."]]></description>
<dc:subject>to:NB visual_display_of_quantitative_information confidence_sets prediction hofman.jake via:? to_teach:linear_models to_teach:undergrad-ADA</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:00972a2320bb/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:confidence_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:hofman.jake"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:linear_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://kjhealy.github.io/gssr/">
    <title>US General Social Survey (GSS) Data for R • gssr</title>
    <dc:date>2021-11-10T18:19:56+00:00</dc:date>
    <link>https://kjhealy.github.io/gssr/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>data_sets sociology to_teach:statistics_of_inequality_and_discrimination to_teach:data_over_space_and_time to_teach:undergrad-ADA healy.kieran</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:c23756feb68d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:sociology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:statistics_of_inequality_and_discrimination"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data_over_space_and_time"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:healy.kieran"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://kjhealy.github.io/gssr/index.html">
    <title>US General Social Survey (GSS) Data for R • gssr</title>
    <dc:date>2021-09-29T13:19:34+00:00</dc:date>
    <link>https://kjhealy.github.io/gssr/index.html</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>R data_sets surveys to_teach:undergrad-ADA to_teach:data_over_space_and_time to_teach:statistics_of_inequality_and_discrimination healy.kieran</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:91dd691c9d70/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:surveys"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data_over_space_and_time"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:statistics_of_inequality_and_discrimination"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:healy.kieran"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1411.5279">
    <title>[1411.5279] What Teachers Should Know about the Bootstrap: Resampling in the Undergraduate Statistics Curriculum</title>
    <dc:date>2021-09-17T14:09:40+00:00</dc:date>
    <link>https://arxiv.org/abs/1411.5279</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["I have three goals in this article: (1) To show the enormous potential of bootstrapping and permutation tests to help students understand statistical concepts including sampling distributions, standard errors, bias, confidence intervals, null distributions, and P-values. (2) To dig deeper, understand why these methods work and when they don't, things to watch out for, and how to deal with these issues when teaching. (3) To change statistical practice---by comparing these methods to common t tests and intervals, we see how inaccurate the latter are; we confirm this with asymptotics. n >= 30 isn't enough---think n >= 5000. Resampling provides diagnostics, and more accurate alternatives. Sadly, the common bootstrap percentile interval badly under-covers in small samples; there are better alternatives. The tone is informal, with a few stories and jokes."]]></description>
<dc:subject>have_read statistics teaching bootstrap to_teach:undergrad-ADA to_teach:statistics_of_inequality_and_discrimination in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:39c04c330c50/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:teaching"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bootstrap"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:statistics_of_inequality_and_discrimination"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.pnas.org/content/117/29/16880.short">
    <title>Universal inference | PNAS</title>
    <dc:date>2021-04-09T19:07:06+00:00</dc:date>
    <link>https://www.pnas.org/content/117/29/16880.short</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We propose a general method for constructing confidence sets and hypothesis tests that have finite-sample guarantees without regularity conditions. We refer to such procedures as “universal.” The method is very simple and is based on a modified version of the usual likelihood-ratio statistic that we call “the split likelihood-ratio test” (split LRT) statistic. The (limiting) null distribution of the classical likelihood-ratio statistic is often intractable when used to test composite null hypotheses in irregular statistical models. Our method is especially appealing for statistical inference in these complex setups. The method we suggest works for any parametric model and also for some nonparametric models, as long as computing a maximum-likelihood estimator (MLE) is feasible under the null. Canonical examples arise in mixture modeling and shape-constrained inference, for which constructing tests and confidence sets has been notoriously difficult. We also develop various extensions of our basic methods. We show that in settings when computing the MLE is hard, for the purpose of constructing valid tests and intervals, it is sufficient to upper bound the maximum likelihood. We investigate some conditions under which our methods yield valid inferences under model misspecification. Further, the split LRT can be used with profile likelihoods to deal with nuisance parameters, and it can also be run sequentially to yield anytime-valid P values and confidence sequences. Finally, when combined with the method of sieves, it can be used to perform model selection with nested model classes."]]></description>
<dc:subject>to:NB have_read hypothesis_testing confidence_sets statistics kith_and_kin wasserman.larry ramdas.aaditya to_teach:undergrad-ADA re:HEAS balakrishnan.sivaraman</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:5825e245a7b6/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:hypothesis_testing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:confidence_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:wasserman.larry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ramdas.aaditya"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:HEAS"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:balakrishnan.sivaraman"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://doi.org/10.1093/oso/9780190900663.001.0001">
    <title>Reproducible Econometrics Using R - Oxford Scholarship</title>
    <dc:date>2021-01-16T07:03:52+00:00</dc:date>
    <link>https://doi.org/10.1093/oso/9780190900663.001.0001</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This book is designed to facilitate reproducibility in Econometrics. It does so by using open source software (R) and recently developed tools (R Markdown and bookdown) that allow the reader to engage in reproducible research. Illustrative examples are provided throughout, and a range of topics are covered. Assignments, exams, slides, and a solution manual are available for instructors."

--- I checked out the library's codex copy just before lockdown and have been slowly, but enjoyably, going through it...]]></description>
<dc:subject>to:NB books:noted to_download to_read racine.jeffrey econometrics R to_teach:undergrad-ADA to_teach:data_over_space_and_time statistics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:66446d8278e8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_download"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:racine.jeffrey"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data_over_space_and_time"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.pnas.org/content/113/38/10530">
    <title>Extracting multistage screening rules from online dating activity data | PNAS</title>
    <dc:date>2021-01-07T21:14:16+00:00</dc:date>
    <link>https://www.pnas.org/content/113/38/10530</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This paper presents a statistical framework for harnessing online activity data to better understand how people make decisions. Building on insights from cognitive science and decision theory, we develop a discrete choice model that allows for exploratory behavior and multiple stages of decision making, with different rules enacted at each stage. Critically, the approach can identify if and when people invoke noncompensatory screeners that eliminate large swaths of alternatives from detailed consideration. The model is estimated using deidentified activity data on 1.1 million browsing and writing decisions observed on an online dating site. We find that mate seekers enact screeners (“deal breakers”) that encode acceptability cutoffs. A nonparametric account of heterogeneity reveals that, even after controlling for a host of observable attributes, mate evaluation differs across decision stages as well as across identified groupings of men and women. Our statistical framework can be widely applied in analyzing large-scale data on multistage choices, which typify searches for “big ticket” items."]]></description>
<dc:subject>to:NB decision-making statistics nonparametrics mixture_models practices_relating_to_the_transmission_of_genetic_information to_teach:undergrad-ADA via:gabriel_rossman feinberg.fred</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a8ecf81231c6/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:nonparametrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mixture_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:practices_relating_to_the_transmission_of_genetic_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:gabriel_rossman"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:feinberg.fred"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2011.14999">
    <title>[2011.14999] An Automatic Finite-Sample Robustness Metric: Can Dropping a Little Data Change Conclusions?</title>
    <dc:date>2020-12-03T16:13:23+00:00</dc:date>
    <link>https://arxiv.org/abs/2011.14999</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We propose a method to assess the sensitivity of econometric analyses to the removal of a small fraction of the sample. Analyzing all possible data subsets of a certain size is computationally prohibitive, so we provide a finite-sample metric to approximately compute the number (or fraction) of observations that has the greatest influence on a given result when dropped. We call our resulting metric the Approximate Maximum Influence Perturbation. Our approximation is automatically computable and works for common estimators (including OLS, IV, GMM, MLE, and variational Bayes). We provide explicit finite-sample error bounds on our approximation for linear and instrumental variables regressions. At minimal computational cost, our metric provides an exact finite-sample lower bound on sensitivity for any estimator, so any non-robustness our metric finds is conclusive. We demonstrate that the Approximate Maximum Influence Perturbation is driven by a low signal-to-noise ratio in the inference problem, is not reflected in standard errors, does not disappear asymptotically, and is not a product of misspecification. Several empirical applications show that even 2-parameter linear regression analyses of randomized trials can be highly sensitive. While we find some applications are robust, in others the sign of a treatment effect can be changed by dropping less than 1% of the sample even when standard errors are small."]]></description>
<dc:subject>to:NB statistics robustness estimation to_read linear_regression to_teach:linear_models to_teach:undergrad-ADA</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:bc72bd101639/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:robustness"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:estimation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:linear_regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:linear_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://ieeexplore.ieee.org/document/8861141">
    <title>How Much Does Your Data Exploration Overfit? Controlling Bias via Information Usage - IEEE Journals &amp; Magazine</title>
    <dc:date>2020-11-16T16:05:49+00:00</dc:date>
    <link>https://ieeexplore.ieee.org/document/8861141</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Modern data is messy and high-dimensional, and it is often not clear a priori what are the right questions to ask. Instead, the analyst typically needs to use the data to search for interesting analyses to perform and hypotheses to test. This is an adaptive process, where the choice of analysis to be performed next depends on the results of the previous analyses on the same data. Ultimately, which results are reported can be heavily influenced by the data. It is widely recognized that this process, even if well-intentioned, can lead to biases and false discoveries, contributing to the crisis of reproducibility in science. But while any data-exploration renders standard statistical theory invalid, experience suggests that different types of exploratory analysis can lead to disparate levels of bias, and the degree of bias also depends on the particulars of the data set. In this paper, we propose a general information usage framework to quantify and provably bound the bias and other error metrics of an arbitrary exploratory analysis. We prove that our mutual information based bound is tight in natural settings, and then use it to give rigorous insights into when commonly used procedures do or do not lead to substantially biased estimation. Through the lens of information usage, we analyze the bias of specific exploration procedures such as filtering, rank selection and clustering. Our general framework also naturally motivates randomization techniques that provably reduce exploration bias while preserving the utility of the data analysis. We discuss the connections between our approach and related ideas from differential privacy and blinded data analysis, and supplement our results with illustrative simulations."

--- Pretty sure I've previously bookmarked a pre-print.]]></description>
<dc:subject>to:NB to_read data_analysis data_mining model_selection statistics post-model-selection_inference to_teach:linear_models to_teach:undergrad-ADA to_teach:data-mining</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:3ef24ab591ab/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_mining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:model_selection"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:post-model-selection_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:linear_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data-mining"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3715610">
    <title>Rain, Rain, Go away: 137 potential exclusion-restriction violations for studies using weather as an instrumental variable by Jonathan Mellon :: SSRN</title>
    <dc:date>2020-10-20T22:25:18+00:00</dc:date>
    <link>https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3715610</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Instrumental variable (IV) analysis assumes that the instrument only affects the dependent variable via its relationship with the independent variable. Other possible causal routes from the IV to the dependent variable are exclusion-restriction violations and make the instrument invalid. Weather has been widely used as an instrumental variable in social science to predict many different variables. The use of weather to instrument different independent variables represents strong prima facie evidence of exclusion violations for all studies using weather as an IV. A review of 185 social science studies (including 111 IV studies) reveals 137 variables which have been linked to weather, all of which represent potential exclusion violations. I conclude with practical steps for systematically reviewing existing literature to identify possible exclusion violations when using IV designs."]]></description>
<dc:subject>causal_inference instrumental_variables via:kjhealy re:ADAfaEPoV have_read to_teach:undergrad-ADA in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:3bcaa9421b2e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:instrumental_variables"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:kjhealy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:ADAfaEPoV"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://obsstudies.org/the-causal-impact-of-bail-on-case-outcomes-for-indigent-defendants-in-new-york-city/">
    <title>The Causal Impact of Bail on Case Outcomes for Indigent Defendants in New York City | Observational Studies</title>
    <dc:date>2020-07-28T18:49:18+00:00</dc:date>
    <link>https://obsstudies.org/the-causal-impact-of-bail-on-case-outcomes-for-indigent-defendants-in-new-york-city/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["It has long been observed that defendants who are subject to pre-trial detention are more likely to be convicted than those who are free while they await trial. However, until recently, much of the literature in this area was only correlative and not causal. Using an instrumental variable that represents judge severity, we apply near-far matching — a statistical methodology designed to assess causal relationships using observational data  –to a dataset of criminal cases that were handled by the New York Legal Aid Society in 2015. We find a strong causal relationship between bail — an obstacle that prevents many from pre-trial release — and case outcome. Specifically, we find setting bail results in a 34% increase in the likelihood of conviction for the cases in our analysis. To our knowledge, this marks the first time matching methodology from the observational studies tradition has been applied to understand the relationship between money bail and the likelihood of conviction."]]></description>
<dc:subject>to:NB prison bail causal_inference instrumental_variables to_teach:undergrad-ADA</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:460d2bab5ebc/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:prison"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bail"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:instrumental_variables"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.ncbi.nlm.nih.gov/pubmed/23371353">
    <title>The table 2 fallacy: presenting and interpreting confounder and modifier coefficients. - PubMed - NCBI</title>
    <dc:date>2020-02-25T15:43:08+00:00</dc:date>
    <link>https://www.ncbi.nlm.nih.gov/pubmed/23371353</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["It is common to present multiple adjusted effect estimates from a single model in a single table. For example, a table might show odds ratios for one or more exposures and also for several confounders from a single logistic regression. This can lead to mistaken interpretations of these estimates. We use causal diagrams to display the sources of the problems. Presentation of exposure and confounder effect estimates from a single model may lead to several interpretative difficulties, inviting confusion of direct-effect estimates with total-effect estimates for covariates in the model. These effect estimates may also be confounded even though the effect estimate for the main exposure is not confounded. Interpretation of these effect estimates is further complicated by heterogeneity (variation, modification) of the exposure effect measure across covariate levels. We offer suggestions to limit potential misunderstandings when multiple effect estimates are presented, including precise distinction between total and direct effect measures from a single model, and use of multiple models tailored to yield total-effect estimates for covariates."]]></description>
<dc:subject>to:NB causal_inference regression statistics greenland.sander to_teach:linear_models to_teach:undergrad-ADA re:ADAfaEPoV</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d60fa39d79d4/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:greenland.sander"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:linear_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:ADAfaEPoV"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://onlinelibrary.wiley.com/doi/abs/10.1111/1468-4446.12711">
    <title>How does cultural capital affect educational performance: Signals or skills? - Breinholt - 2020 - The British Journal of Sociology - Wiley Online Library</title>
    <dc:date>2020-01-13T20:33:06+00:00</dc:date>
    <link>https://onlinelibrary.wiley.com/doi/abs/10.1111/1468-4446.12711</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In this paper, we test two mechanisms through which cultural capital might affect educational performance: (a) teachers misinterpreting cultural capital as signals of academic brilliance and (b) cultural capital fostering skills in children that enhance educational performance. We analyse data from the ECLS‐K and ECLS‐K:2011 from the United States and focus on three aspects of children’s cultural capital: participation in performing arts, reading interest and participation in athletics and clubs. We find that (1) none of the three aspects of cultural capital that we consider affects teachers’ evaluations of children’s academic skills; (2) reading interest has a direct positive effect on educational performance; and (3) the direct effect of reading interest on educational performance does not depend on schooling context. Our results provide little support for the hypothesis that cultural capital operates via signals about academic brilliance. Instead, they suggest that cultural capital fosters skills in children that enhance educational performance. We discuss the theoretical implications of our findings."

--- Replication data is supposedly available.]]></description>
<dc:subject>to:NB to_read education causal_inference sociology statistics to_teach:undergrad-ADA</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:236c453a1910/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:education"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:sociology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3273476">
    <title>Generalizable and Robust TV Advertising Effects by Bradley Shapiro, Günter J. Hitsch, Anna Tuchman :: SSRN</title>
    <dc:date>2020-01-09T17:43:36+00:00</dc:date>
    <link>https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3273476</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We provide generalizable and robust results on the causal sales effect of TV advertising for a large number of products in many categories. Such generalizable results provide a prior distribution that can improve the advertising decisions made by firms and the analysis and recommendations of policy makers. A single case study cannot provide generalizable results, and hence the literature provides several meta-analyses based on published case studies of advertising effects. However, publication bias results if the research or review process systematically rejects estimates of small, statistically insignificant, or “unexpected” advertising elasticities. Consequently, if there is publication bias, the results of a meta-analysis will not reflect the true population distribution of advertising effects. To provide generalizable results, we base our analysis on a large number of products and clearly lay out the research protocol used to select the products. We characterize the distribution of all estimates, irrespective of sign, size, or statistical significance. To ensure generalizability, we document the robustness of the estimates. First, we examine the sensitivity of the results to the assumptions made when constructing the data used in estimation. Second, we document whether the estimated effects are sensitive to the identification strategies that we use to claim causality based on observational data. Our results reveal substantially smaller advertising elasticities compared to the results documented in the extant literature, as well as a sizable percentage of statistically insignificant or negative estimates. If we only select products with statistically significant and positive estimates, the mean and median of the advertising effect distribution increase by a factor of about five. The results are robust to various identifying assumptions, and are consistent with both publication bias and bias due to non-robust identification strategies to obtain causal estimates in the literature."]]></description>
<dc:subject>to:NB causal_inference econometrics statistics advertising to_teach:undergrad-ADA</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:6309f471803b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:advertising"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1909.06539">
    <title>[1909.06539] Not again! Data Leakage in Digital Pathology</title>
    <dc:date>2019-10-01T17:12:30+00:00</dc:date>
    <link>https://arxiv.org/abs/1909.06539</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Bioinformatics of high throughput omics data (e.g. microarrays and proteomics) has been plagued by uncountable issues with reproducibility at the start of the century. Concerns have motivated international initiatives such as the FDA's led MAQC Consortium, addressing reproducibility of predictive biomarkers by means of appropriate Data Analysis Plans (DAPs). For instance, repreated cross-validation is a standard procedure meant at mitigating the risk that information from held-out validation data may be used during model selection. We prove here that, many years later, Data Leakage can still be a non-negligible overfitting source in deep learning models for digital pathology. In particular, we evaluate the impact of (i) the presence of multiple images for each subject in histology collections; (ii) the systematic adoption of training over collection of subregions (i.e. "tiles" or "patches") extracted for the same subject. We verify that accuracy scores may be inflated up to 41%, even if a well-designed 10x5 iterated cross-validation DAP is applied, unless all images from the same subject are kept together either in the internal training or validation splits. Results are replicated for 4 classification tasks in digital pathology on 3 datasets, for a total of 373 subjects, and 543 total slides (around 27, 000 tiles). Impact of applying transfer learning strategies with models pre-trained on general-purpose or digital pathology datasets is also discussed."]]></description>
<dc:subject>to:NB cross-validation statistics bad_data_analysis to_teach:undergrad-ADA to_teach:data-mining</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:55f36f7dc31d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cross-validation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bad_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data-mining"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1901.01241">
    <title>[1901.01241] Nonparametric Instrumental Variables Estimation Under Misspecification</title>
    <dc:date>2019-08-29T00:47:22+00:00</dc:date>
    <link>https://arxiv.org/abs/1901.01241</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We show that nonparametric instrumental variables estimators are highly sensitive to misspecification: an arbitrarily small deviation from instrumental validity can lead to large asymptotic bias for a broad class of estimators. The problem is mitigated if strong restrictions on the structural function are imposed in estimation. However, if the true function does not obey the restrictions, then imposing them imparts bias. Therefore, there is a trade-off between the sensitivity to invalid instruments and bias from imposing excessive restrictions. We propose a partial identification approach that allows a researcher to explicitly and transparently examine this trade-off and make inferences about the structural function that are valid under a small failure of instrumental validity. We construct a simple, consistent estimator of the identified set. We apply our methods to the empirical setting of Blundell et al. (2007) and Horowitz (2011) to estimate shape-invariant Engel curves."]]></description>
<dc:subject>instrumental_variables causal_inference nonparametrics statistics to_teach:undergrad-ADA in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:14e1c2d3467e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:instrumental_variables"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:nonparametrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.annualreviews.org/doi/abs/10.1146/annurev-economics-080218-025651">
    <title>Bootstrap Methods in Econometrics | Annual Review of Economics</title>
    <dc:date>2019-08-26T23:51:42+00:00</dc:date>
    <link>https://www.annualreviews.org/doi/abs/10.1146/annurev-economics-080218-025651</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The bootstrap is a method for estimating the distribution of an estimator or test statistic by resampling one's data or a model estimated from the data. Under conditions that hold in a wide variety of econometric applications, the bootstrap provides approximations to distributions of statistics, coverage probabilities of confidence intervals, and rejection probabilities of hypothesis tests that are more accurate than the approximations of first-order asymptotic distribution theory. The reductions in the differences between true and nominal coverage or rejection probabilities can be very large. In addition, the bootstrap provides a way to carry out inference in certain settings where obtaining analytic distributional approximations is difficult or impossible. This article explains the usefulness and limitations of the bootstrap in contexts of interest in econometrics. The presentation is informal and expository. It provides an intuitive understanding of how the bootstrap works. Mathematical details are available in the references that are cited."]]></description>
<dc:subject>to:NB bootstrap statistics economics to_teach:undergrad-ADA horowitz.joel</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:5aff30353151/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bootstrap"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:horowitz.joel"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.jstatsoft.org/article/view/v090i12">
    <title>Evaluating Probabilistic Forecasts with scoringRules | Jordan | Journal of Statistical Software</title>
    <dc:date>2019-08-21T13:26:25+00:00</dc:date>
    <link>https://www.jstatsoft.org/article/view/v090i12</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Probabilistic forecasts in the form of probability distributions over future events have become popular in several fields including meteorology, hydrology, economics, and demography. In typical applications, many alternative statistical models and data sources can be used to produce probabilistic forecasts. Hence, evaluating and selecting among competing methods is an important task. The scoringRules package for R provides functionality for comparative evaluation of probabilistic models based on proper scoring rules, covering a wide range of situations in applied work. This paper discusses implementation and usage details, presents case studies from meteorology and economics, and points to the relevant background literature."]]></description>
<dc:subject>prediction statistics to_teach:undergrad-ADA to_teach:data-mining scoring_rules in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:405f6f61745b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data-mining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:scoring_rules"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.sciencedirect.com/science/article/pii/S2352827319300096?via%3Dihub">
    <title>Free trade and opioid overdose death in the United States - ScienceDirect</title>
    <dc:date>2019-07-29T18:47:52+00:00</dc:date>
    <link>https://www.sciencedirect.com/science/article/pii/S2352827319300096?via%3Dihub</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Opioid overdose deaths in the U.S. rose dramatically after 1999, but also exhibited substantial geographic variation. This has largely been explained by differential availability of prescription and non-prescription opioids, including heroin and fentanyl. Recent studies explore the underlying role of socioeconomic factors, but overlook the influence of job loss due to international trade, an economic phenomenon that disproportionately harms the same regions and demographic groups at the heart of the opioid epidemic. We used OLS regression and county-year level data from the Centers for Disease Controls and the Department of Labor to test the association between trade-related job loss and opioid-related overdose death between 1999 and 2015. We find that the loss of 1000 trade-related jobs was associated with a 2.7 percent increase in opioid-related deaths. When fentanyl was present in the heroin supply, the same number of job losses was associated with a 11.3 percent increase in opioid-related deaths."

--- I'm very skeptical about OLS here.  Something like nearest neighbors would be better here, but I'm not sure how to handle spatial correlation.]]></description>
<dc:subject>to:NB to_read drugs whats_gone_wrong_with_america class_struggles_in_america econometrics statistics globalization to_teach:data_over_space_and_time to_teach:undergrad-ADA causal_inference</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:56271b63f2a4/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:drugs"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:whats_gone_wrong_with_america"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:class_struggles_in_america"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:globalization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data_over_space_and_time"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.tandfonline.com/doi/full/10.1080/10618600.2019.1629942">
    <title>Scalable Visualization Methods for Modern Generalized Additive Models: Journal of Computational and Graphical Statistics: Vol 0, No 0</title>
    <dc:date>2019-07-24T14:15:06+00:00</dc:date>
    <link>https://www.tandfonline.com/doi/full/10.1080/10618600.2019.1629942</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In the last two decades, the growth of computational resources has made it possible to handle generalized additive models (GAMs) that formerly were too costly for serious applications. However, the growth in model complexity has not been matched by improved visualizations for model development and results presentation. Motivated by an industrial application in electricity load forecasting, we identify the areas where the lack of modern visualization tools for GAMs is particularly severe, and we address the shortcomings of existing methods by proposing a set of visual tools that (a) are fast enough for interactive use, (b) exploit the additive structure of GAMs, (c) scale to large data sets, and (d) can be used in conjunction with a wide range of response distributions. The new visual methods proposed here are implemented by the mgcViz R package, available on the Comprehensive R Archive Network. Supplementary materials for this article are available online."]]></description>
<dc:subject>to:NB additive_models visual_display_of_quantitative_information computational_statistics statistics R to_teach:undergrad-ADA</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:4954db6bc43a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:additive_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:computational_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.aeaweb.org/articles?id=10.1257/app.20160056">
    <title>Life after Lead: Effects of Early Interventions for Children Exposed to Lead</title>
    <dc:date>2019-06-10T22:39:07+00:00</dc:date>
    <link>https://www.aeaweb.org/articles?id=10.1257/app.20160056</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Lead pollution is consistently linked to cognitive and behavioral impairments, yet little is known about the benefits of public health interventions for children exposed to lead. This paper estimates the long-term impacts of early-life interventions (e.g. lead remediation, nutritional assessment, medical evaluation, developmental surveillance, and public assistance referrals) recommended for lead-poisoned children. Using linked administrative data from Charlotte, NC, we compare outcomes for children who are similar across observable characteristics but differ in eligibility for intervention due to blood lead test results. We find that the negative outcomes previously associated with early-life exposure can largely be reversed by intervention."

--- The last tag, as usual, is conditional on liking the paper after reading it, and on replication data being available.]]></description>
<dc:subject>to:NB to_read lead cognitive_development sociology causal_inference to_teach:undergrad-ADA</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:dd8b1936a46a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:lead"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_development"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:sociology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.annualreviews.org/doi/abs/10.1146/annurev-soc-073117-041429">
    <title>Interpreting and Understanding Logits, Probits, and Other Nonlinear Probability Models | Annual Review of Sociology</title>
    <dc:date>2019-05-26T17:57:53+00:00</dc:date>
    <link>https://www.annualreviews.org/doi/abs/10.1146/annurev-soc-073117-041429</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Methods textbooks in sociology and other social sciences routinely recommend the use of the logit or probit model when an outcome variable is binary, an ordered logit or ordered probit when it is ordinal, and a multinomial logit when it has more than two categories. But these methodological guidelines take little or no account of a body of work that, over the past 30 years, has pointed to problematic aspects of these nonlinear probability models and, particularly, to difficulties in interpreting their parameters. In this review, we draw on that literature to explain the problems, show how they manifest themselves in research, discuss the strengths and weaknesses of alternatives that have been suggested, and point to lines of further analysis."]]></description>
<dc:subject>to:NB statistics classifiers bad_data_analysis to_teach:undergrad-ADA</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:fca0788514e6/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:classifiers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bad_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://stats.stackexchange.com/questions/287144/variance-of-a-sample-covariance-for-normal-variables">
    <title>estimation - Variance of a sample covariance for normal variables - Cross Validated</title>
    <dc:date>2019-05-22T23:11:28+00:00</dc:date>
    <link>https://stats.stackexchange.com/questions/287144/variance-of-a-sample-covariance-for-normal-variables</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[To make into an exercise.  (As one of the answers points out, there is nothing here which turns on using a Gaussian distribution.)]]></description>
<dc:subject>probability statistics to_teach:undergrad-ADA to_teach:linear_models to_teach:data_over_space_and_time</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:5dc6ef433a3b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:probability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:linear_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data_over_space_and_time"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1904.02438">
    <title>[1904.02438] Cross-Validation for Correlated Data</title>
    <dc:date>2019-04-08T21:50:30+00:00</dc:date>
    <link>https://arxiv.org/abs/1904.02438</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["K-fold cross-validation (CV) with squared error loss is widely used for evaluating predictive models, especially when strong distributional data assumptions cannot be taken. However, CV with squared error loss is not free from distributional assumptions, in particular in cases involving non-i.i.d data. This paper analyzes CV for correlated data. We present a criterion for suitability of CV, and introduce a bias corrected cross-validation prediction error estimator, CVc, which is suitable in many settings involving correlated data, where CV is invalid. Our theoretical results are also demonstrated numerically."

--- ETA after reading: I don't see why this is better than the approach taken in the earlier literature (like buffers)]]></description>
<dc:subject>to:NB statistics cross-validation time_series rosset.saharon to_teach:undergrad-ADA to_teach:data_over_space_and_time have_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:bb3ea15ebfce/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cross-validation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:time_series"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:rosset.saharon"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data_over_space_and_time"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3336338">
    <title>The Bias Is Built In: How Administrative Records Mask Racially Biased Policing by Dean Knox, Will Lowe, Jonathan Mummolo :: SSRN</title>
    <dc:date>2019-02-20T15:12:47+00:00</dc:date>
    <link>https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3336338</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Researchers often lack the necessary data to credibly estimate racial bias in policing. In particular, police administrative records lack information on civilians that police observe but do not investigate. In this paper, we show that if police racially discriminate when choosing whom to investigate, using administrative records to estimate racial bias in police behavior amounts to post-treatment conditioning, and renders many quantities of interest unidentified---even among investigated individuals---absent strong and untestable assumptions. In most cases, no set of controls can eliminate this statistical bias, the exact form of which we derive through principal stratification in a causal mediation framework. We develop a bias-correction procedure and nonparametric sharp bounds for race effects, replicate published findings, and show traditional estimation techniques can severely underestimate levels of racially biased policing or even mask discrimination entirely. We conclude by outlining a general and feasible design for future studies that is robust to this inferential snare."]]></description>
<dc:subject>to:NB to_read causal_inference police discrimination statistics to_teach:undergrad-ADA via:henry_farrell</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:45a5bfb2c045/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:police"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:discrimination"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:henry_farrell"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://notstatschat.rbind.io/2015/09/14/high-dimensional-space-is-big./">
    <title>(high-dimensional) Space is Big. - Biased and Inefficient</title>
    <dc:date>2019-02-01T15:28:35+00:00</dc:date>
    <link>https://notstatschat.rbind.io/2015/09/14/high-dimensional-space-is-big./</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>have_read high-dimensional_probability high-dimensional_statistics lumley.thomas to_teach:undergrad-ADA to_teach:data-mining</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:3f3c41b0fa19/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:high-dimensional_probability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:high-dimensional_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:lumley.thomas"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data-mining"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://notstatschat.rbind.io/2019/02/01/recognising-when-you-don-t-know/">
    <title>Recognising when you don’t know - Biased and Inefficient</title>
    <dc:date>2019-02-01T15:14:25+00:00</dc:date>
    <link>https://notstatschat.rbind.io/2019/02/01/recognising-when-you-don-t-know/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[(Some nice shade is thrown on the difference between machine learning and statistics --- excuse me, "data science".)]]></description>
<dc:subject>classifiers mushrooms statistics to_teach:data-mining to_teach:undergrad-ADA lumley.thomas</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d7acee587f23/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:classifiers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mushrooms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data-mining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:lumley.thomas"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.aeaweb.org/articles?id=10.1257/aer.20160600">
    <title>The Taxing Deed of Globalization</title>
    <dc:date>2019-01-30T15:06:18+00:00</dc:date>
    <link>https://www.aeaweb.org/articles?id=10.1257/aer.20160600</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This paper examines the effects of globalization on the distribution of worker-specific labor taxes using a unique set of tax calculators. We find a differential effect of higher trade and factor mobility on relative tax burdens in 1980–1993 versus 1994–2007 in the OECD. Prior to 1994, greater openness meant that higher income earners were taxed progressively more. However, after 1994, we document a globalization-induced rise in the labor income tax burden of the middle class, while the top 1 percent of workers and employees faced a reduction in their tax burden of 0.59–1.45 percentage points."]]></description>
<dc:subject>to:NB globalization economics to_teach:undergrad-ADA</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:232d64ec6def/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:globalization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.nature.com/articles/s41562-018-0506-1">
    <title>The association between adolescent well-being and digital technology use | Nature Human Behaviour</title>
    <dc:date>2019-01-15T13:41:43+00:00</dc:date>
    <link>https://www.nature.com/articles/s41562-018-0506-1</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The widespread use of digital technologies by young people has spurred speculation that their regular use negatively impacts psychological well-being. Current empirical evidence supporting this idea is largely based on secondary analyses of large-scale social datasets. Though these datasets provide a valuable resource for highly powered investigations, their many variables and observations are often explored with an analytical flexibility that marks small effects as statistically significant, thereby leading to potential false positives and conflicting results. Here we address these methodological challenges by applying specification curve analysis (SCA) across three large-scale social datasets (total n = 355,358) to rigorously examine correlational evidence for the effects of digital technology on adolescents. The association we find between digital technology use and adolescent well-being is negative but small, explaining at most 0.4% of the variation in well-being. Taking the broader context of the data into account suggests that these effects are too small to warrant policy change."

]]></description>
<dc:subject>networked_life sociology statistics model_checking to_teach:undergrad-ADA re:actually-dr-internet-is-the-name-of-the-monsters-creator social_media to_teach:data-mining social_measurement psychometrics measurement in_NB have_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ac7d460ad002/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networked_life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:sociology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:model_checking"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:actually-dr-internet-is-the-name-of-the-monsters-creator"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_media"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data-mining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_measurement"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:psychometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:measurement"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/4037">
    <title>Youth-Parent Socialization Panel Study, 1965-1997: Four Waves Combined</title>
    <dc:date>2019-01-06T19:50:41+00:00</dc:date>
    <link>https://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/4037</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The Youth-Parent Socialization Panel Study is a series of surveys designed to assess political continuity and change across time for biologically-related generations and to gauge the impact of life-stage events and historical trends on the behaviors and attitudes of respondents. A national sample of high school seniors and their parents was first surveyed in 1965. Subsequent surveys of the same individuals were conducted in 1973, 1982, and 1997. This data collection combines all four waves of youth data for the study. The general objective of the data collection was to study the dynamics of political attitudes and behaviors by obtaining data on the same individuals as they aged from approximately 18 years of age in 1965 to 50 years of age in 1997. Especially when combined with other elements of the study as released in other ICPSR collections in the Youth Studies Series, this data collection facilitates the analysis of generational, life cycle, and historical effects and political influences on relationships within the family. This data collection also has several distinctive properties. First, it is a longitudinal study of a particular cohort, a national sample from the graduating high school class of 1965. Second, it captures the respondents at key points in their life stages -- at ages 18, 26, 35, and 50. Third, the dataset contains many replicated measures over time as well as some measures unique to each data point. Fourth, there is detailed information about the respondents' life histories. Background variables include age, sex, religious orientation, level of religious participation, marital status, ethnicity, educational status and background, place of residence, family income, and employment status."

--- Used in Rochon's book about value change, in a way which would make it a good case study for propensity-score matching (which Rochon did _not_ do, confounding his inferences).  Query, can I get access via CMU, or are we not part of the consortium?]]></description>
<dc:subject>data_sets us_politics public_opinion to_teach:undergrad-ADA</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:bfea183bf8b8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:us_politics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:public_opinion"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.mitpressjournals.org/doi/abs/10.1162/rest_a_00754">
    <title>Robots at Work | The Review of Economics and Statistics | MIT Press Journals</title>
    <dc:date>2019-01-04T03:25:31+00:00</dc:date>
    <link>https://www.mitpressjournals.org/doi/abs/10.1162/rest_a_00754</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We analyze for the first time the economic contributions of modern industrial robots, which are flexible, versatile, and autonomous machines. We use novel panel data on robot adoption within industries in seventeen countries from 1993 to 2007 and new instrumental variables that rely on robots’ comparative advantage in specific tasks. Our findings suggest that increased robot use contributed approximately 0.36 percentage points to annual labor productivity growth, while at the same time raising total factor productivity and lowering output prices. Our estimates also suggest that robots did not significantly reduce total employment, although they did reduce low-skilled workers’ employment share."

- Last tag for the instrumental variables (if they look sensible and perhaps especially if they do not)]]></description>
<dc:subject>to:NB economics instrumental_variables robots_and_robotics to_teach:undergrad-ADA</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:591f82814b6c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:instrumental_variables"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:robots_and_robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.fromthebottomoftheheap.net/2018/12/10/confidence-intervals-for-glms/">
    <title>Confidence intervals for GLMs</title>
    <dc:date>2018-12-11T13:28:10+00:00</dc:date>
    <link>https://www.fromthebottomoftheheap.net/2018/12/10/confidence-intervals-for-glms/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[For the trick about finding the inverse link function.]]></description>
<dc:subject>regression R to_teach:undergrad-ADA via:kjhealy</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ecc65569c27d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:kjhealy"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.iza.org/publications/dp/11900/the-effect-of-media-coverage-on-mass-shootings">
    <title>The Effect of Media Coverage on Mass Shootings | IZA - Institute of Labor Economics</title>
    <dc:date>2018-12-07T14:23:47+00:00</dc:date>
    <link>https://www.iza.org/publications/dp/11900/the-effect-of-media-coverage-on-mass-shootings</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Can media coverage of shooters encourage future mass shootings? We explore the link between the day-to-day prime time television news coverage of shootings on ABC World News Tonight and subsequent mass shootings in the US from January 1, 2013 to June 23, 2016. To circumvent latent endogeneity concerns, we employ an instrumental variable strategy: worldwide disaster deaths provide an exogenous variation that systematically crowds out shooting-related coverage. Our findings consistently suggest a positive and statistically significant effect of coverage on the number of subsequent shootings, lasting for 4-10 days. At its mean, news coverage is suggested to cause approximately three mass shootings in the following week, which would explain 55 percent of all mass shootings in our sample. Results are qualitatively consistent when using (i) additional keywords to capture shooting-related news coverage, (ii) alternative definitions of mass shootings, (iii) the number of injured or killed people as the dependent variable, and (iv) an alternative, longer data source for mass shootings from 2006-2016."]]></description>
<dc:subject>to:NB to_read contagion causal_inference to_teach:undergrad-ADA previous_tag_was_in_poor_taste color_me_skeptical</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:e4a4ae1e675a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:contagion"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:previous_tag_was_in_poor_taste"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.economist.com/graphic-detail/2018/11/03/how-to-forecast-an-americans-vote">
    <title>How to forecast an American’s vote - All politics is identity politics</title>
    <dc:date>2018-11-07T16:29:23+00:00</dc:date>
    <link>https://www.economist.com/graphic-detail/2018/11/03/how-to-forecast-an-americans-vote</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[This looks like a nice case-study for when I teach logistic regression in the spring, provided there's replication data.  It'd be even better if there was a follow-up on how well this actually predicted!]]></description>
<dc:subject>track_down_references logistic_regression us_politics to_teach:undergrad-ADA</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:c69ef96d1518/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:track_down_references"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:logistic_regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:us_politics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://ggdag.netlify.com/">
    <title>Analyze and Create Elegant Directed Acyclic Graphs • ggdag</title>
    <dc:date>2018-08-09T18:28:25+00:00</dc:date>
    <link>https://ggdag.netlify.com/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["ggdag: An R Package for visualizing and analyzing directed acyclic graphs"]]></description>
<dc:subject>R graphical_models visual_display_of_quantitative_information via:arsyed to_teach:undergrad-ADA re:ADAfaEPoV</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:3545bb27c7de/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:graphical_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:arsyed"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:ADAfaEPoV"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://tidymodels.github.io/rsample/">
    <title>General Resampling Infrastructure • rsample</title>
    <dc:date>2018-08-03T14:21:45+00:00</dc:date>
    <link>https://tidymodels.github.io/rsample/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["rsample contains a set of functions that can create different types of resamples and corresponding classes for their analysis. The goal is to have a modular set of methods that can be used across different R packages for:
"traditional resampling techniques for estimating the sampling distribution of a statistic and
"estimating model performance using a holdout set
"The scope of rsample is to provide the basic building blocks for creating and analyzing resamples of a data set but does not include code for modeling or calculating statistics. The “Working with Resample Sets” vignette gives demonstrations of how rsample tools can be used."]]></description>
<dc:subject>to:NB R computational_statistics to_teach:statcomp to_teach:undergrad-ADA via:?</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:1501cde525fa/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:computational_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:statcomp"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1706.08576">
    <title>[1706.08576] Invariant Causal Prediction for Nonlinear Models</title>
    <dc:date>2018-05-18T01:11:07+00:00</dc:date>
    <link>https://arxiv.org/abs/1706.08576</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["An important problem in many domains is to predict how a system will respond to interventions. This task is inherently linked to estimating the system's underlying causal structure. To this end, 'invariant causal prediction' (ICP) (Peters et al., 2016) has been proposed which learns a causal model exploiting the invariance of causal relations using data from different environments. When considering linear models, the implementation of ICP is relatively straight-forward. However, the nonlinear case is more challenging due to the difficulty of performing nonparametric tests for conditional independence. In this work, we present and evaluate an array of methods for nonlinear and nonparametric versions of ICP for learning the causal parents of given target variables. We find that an approach which first fits a nonlinear model with data pooled over all environments and then tests for differences between the residual distributions across environments is quite robust across a large variety of simulation settings. We call this procedure "Invariant residual distribution test". In general, we observe that the performance of all approaches is critically dependent on the true (unknown) causal structure and it becomes challenging to achieve high power if the parental set includes more than two variables. As a real-world example, we consider fertility rate modelling which is central to world population projections. We explore predicting the effect of hypothetical interventions using the accepted models from nonlinear ICP. The results reaffirm the previously observed central causal role of child mortality rates."]]></description>
<dc:subject>to:NB causal_inference causal_discovery statistics regression prediction peters.jonas meinshausen.nicolai to_read heard_the_talk to_teach:undergrad-ADA re:ADAfaEPoV</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:c56e1a37ba95/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_discovery"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:peters.jonas"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:meinshausen.nicolai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:heard_the_talk"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:ADAfaEPoV"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1501.01332">
    <title>[1501.01332] Causal inference using invariant prediction: identification and confidence intervals</title>
    <dc:date>2018-05-18T01:10:13+00:00</dc:date>
    <link>https://arxiv.org/abs/1501.01332</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["What is the difference of a prediction that is made with a causal model and a non-causal model? Suppose we intervene on the predictor variables or change the whole environment. The predictions from a causal model will in general work as well under interventions as for observational data. In contrast, predictions from a non-causal model can potentially be very wrong if we actively intervene on variables. Here, we propose to exploit this invariance of a prediction under a causal model for causal inference: given different experimental settings (for example various interventions) we collect all models that do show invariance in their predictive accuracy across settings and interventions. The causal model will be a member of this set of models with high probability. This approach yields valid confidence intervals for the causal relationships in quite general scenarios. We examine the example of structural equation models in more detail and provide sufficient assumptions under which the set of causal predictors becomes identifiable. We further investigate robustness properties of our approach under model misspecification and discuss possible extensions. The empirical properties are studied for various data sets, including large-scale gene perturbation experiments."

]]></description>
<dc:subject>to:NB to_read causal_inference causal_discovery statistics prediction regression buhlmann.peter meinshausen.nicolai peters.jonas heard_the_talk re:ADAfaEPoV to_teach:undergrad-ADA</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:e39d59855089/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_discovery"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:buhlmann.peter"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:meinshausen.nicolai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:peters.jonas"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:heard_the_talk"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:ADAfaEPoV"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.aeaweb.org/articles?id=10.1257/aer.20141406">
    <title>Family Ruptures, Stress, and the Mental Health of the Next Generation</title>
    <dc:date>2018-03-30T15:51:15+00:00</dc:date>
    <link>https://www.aeaweb.org/articles?id=10.1257/aer.20141406</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This paper studies how in utero exposure to maternal stress from family ruptures affects later mental health. We find that prenatal exposure to the death of a maternal relative increases take-up of ADHD medications during childhood and anti-anxiety and depression medications in adulthood. Further, family ruptures during pregnancy depress birth outcomes and raise the risk of perinatal complications necessitating hospitalization. Our results suggest large welfare gains from preventing fetal stress from family ruptures and possibly from economically induced stressors such as unemployment. They further suggest that greater stress exposure among the poor may partially explain the intergenerational persistence of poverty."

See also an important comment (http://dx.doi.org/10.1257/aer.20161124) and reply (http://dx.doi.org/10.1257/aer.20161605) --- potentially the makings of a very good problem set, if data &c. check out.]]></description>
<dc:subject>to:NB causal_inference inequality economics to_teach:undergrad-ADA</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:1c184b51d04e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:inequality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.nytimes.com/2017/11/22/business/laptops-not-during-lecture-or-meeting.html">
    <title>Laptops Are Great. But Not During a Lecture or a Meeting. - The New York Times</title>
    <dc:date>2018-01-30T17:14:05+00:00</dc:date>
    <link>https://www.nytimes.com/2017/11/22/business/laptops-not-during-lecture-or-meeting.html</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>have_read pedagogy to_teach:undergrad-ADA</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:09dac3b3c377/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:pedagogy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.aeaweb.org/articles?id=10.1257/aer.20151720">
    <title>How Do Hours Worked Vary with Income? Cross-Country Evidence and Implications</title>
    <dc:date>2018-01-24T23:24:16+00:00</dc:date>
    <link>https://www.aeaweb.org/articles?id=10.1257/aer.20151720</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This paper builds a new internationally comparable database of hours worked to measure how hours vary with income across and within countries. We document that average hours worked per adult are substantially higher in low-income countries than in high-income countries. The pattern of decreasing hours with aggregate income holds for both men and women, for adults of all ages and education levels, and along both the extensive and intensive margin. Within countries, hours worked per worker are also decreasing in the individual wage for most countries, though in the richest countries, hours worked are flat or increasing in the wage. One implication of our findings is that aggregate productivity and welfare differences across countries are larger than currently thought."

--- Last tag depends on availability of replication data.]]></description>
<dc:subject>to:NB economics labor to_teach:undergrad-ADA</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:52a344bbcd74/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:labor"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1711.07137">
    <title>[1711.07137] Nonparametric Double Robustness</title>
    <dc:date>2018-01-22T14:56:49+00:00</dc:date>
    <link>https://arxiv.org/abs/1711.07137</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Use of nonparametric techniques (e.g., machine learning, kernel smoothing, stacking) are increasingly appealing because they do not require precise knowledge of the true underlying models that generated the data under study. Indeed, numerous authors have advocated for their use with standard methods (e.g., regression, inverse probability weighting) in epidemiology. However, when used in the context of such singly robust approaches, nonparametric methods can lead to suboptimal statistical properties, including inefficiency and no valid confidence intervals. Using extensive Monte Carlo simulations, we show how doubly robust methods offer improvements over singly robust approaches when implemented via nonparametric methods. We use 10,000 simulated samples and 50, 100, 200, 600, and 1200 observations to investigate the bias and mean squared error of singly robust (g Computation, inverse probability weighting) and doubly robust (augmented inverse probability weighting, targeted maximum likelihood estimation) estimators under four scenarios: correct and incorrect model specification; and parametric and nonparametric estimation. As expected, results show best performance with g computation under correctly specified parametric models. However, even when based on complex transformed covariates, double robust estimation performs better than singly robust estimators when nonparametric methods are used. Our results suggest that nonparametric methods should be used with doubly instead of singly robust estimation techniques."]]></description>
<dc:subject>to:NB statistics causal_inference estimation nonparametrics to_teach:undergrad-ADA kith_and_kin</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:9051959e1bda/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:estimation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:nonparametrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.pnas.org/content/115/2/E144.abstract.html">
    <title>Quantitative historical analysis uncovers a single dimension of complexity that structures global variation in human social organization</title>
    <dc:date>2018-01-09T20:46:25+00:00</dc:date>
    <link>http://www.pnas.org/content/115/2/E144.abstract.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Do human societies from around the world exhibit similarities in the way that they are structured, and show commonalities in the ways that they have evolved? These are long-standing questions that have proven difficult to answer. To test between competing hypotheses, we constructed a massive repository of historical and archaeological information known as “Seshat: Global History Databank.” We systematically coded data on 414 societies from 30 regions around the world spanning the last 10,000 years. We were able to capture information on 51 variables reflecting nine characteristics of human societies, such as social scale, economy, features of governance, and information systems. Our analyses revealed that these different characteristics show strong relationships with each other and that a single principal component captures around three-quarters of the observed variation. Furthermore, we found that different characteristics of social complexity are highly predictable across different world regions. These results suggest that key aspects of social organization are functionally related and do indeed coevolve in predictable ways. Our findings highlight the power of the sciences and humanities working together to rigorously test hypotheses about general rules that may have shaped human history."

--- Contributed, so the last tag applies very forcefully.]]></description>
<dc:subject>to:NB to_read comparative_history complexity_measures principal_components to_teach:undergrad-ADA color_me_skeptical</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:fc1b60107b6e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:comparative_history"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:principal_components"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.mitpressjournals.org/doi/abs/10.1162/neco_a_01021">
    <title>Capturing the Dynamical Repertoire of Single Neurons with Generalized Linear Models | Neural Computation | MIT Press Journals</title>
    <dc:date>2017-12-01T16:01:05+00:00</dc:date>
    <link>http://www.mitpressjournals.org/doi/abs/10.1162/neco_a_01021</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["A key problem in computational neuroscience is to find simple, tractable models that are nevertheless flexible enough to capture the response properties of real neurons. Here we examine the capabilities of recurrent point process models known as Poisson generalized linear models (GLMs). These models are defined by a set of linear filters and a point nonlinearity and are conditionally Poisson spiking. They have desirable statistical properties for fitting and have been widely used to analyze spike trains from electrophysiological recordings. However, the dynamical repertoire of GLMs has not been systematically compared to that of real neurons. Here we show that GLMs can reproduce a comprehensive suite of canonical neural response behaviors, including tonic and phasic spiking, bursting, spike rate adaptation, type I and type II excitation, and two forms of bistability. GLMs can also capture stimulus-dependent changes in spike timing precision and reliability that mimic those observed in real neurons, and can exhibit varying degrees of stochasticity, from virtually deterministic responses to greater-than-Poisson variability. These results show that Poisson GLMs can exhibit a wide range of dynamic spiking behaviors found in real neurons, making them well suited for qualitative dynamical as well as quantitative statistical studies of single-neuron and population response properties."]]></description>
<dc:subject>to:NB neural_data_analysis statistics to_teach:undergrad-ADA pillow.jonathan</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:8dda715fe6f2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:neural_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:pillow.jonathan"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://personal.lse.ac.uk/YoungA/CWOI.pdf">
    <title>Consistency without Inference: Instrumental Variables in Practical Application</title>
    <dc:date>2017-11-14T15:30:41+00:00</dc:date>
    <link>https://personal.lse.ac.uk/YoungA/CWOI.pdf</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["I use the bootstrap to study a comprehensive sample of 1400 instrumental
variables regressions in 32 papers published in the journals of the American
Economic Association. IV estimates are more often found to be falsely significant
and more sensitive to outliers than OLS, while having a higher mean squared error
around the IV population moment. There is little evidence that OLS estimates are
substantively biased, while IV instruments often appear to be irrelevant. In
addition, I find that established weak instrument pre-tests are largely
uninformative and weak instrument robust methods generally perform no better or
substantially worse than 2SLS. "]]></description>
<dc:subject>re:ADAfaEPoV to_teach:undergrad-ADA instrumental_variables causal_inference regression statistics econometrics via:kjhealy have_read in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:0762e8318a2c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:ADAfaEPoV"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:instrumental_variables"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:kjhealy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1706.09141">
    <title>[1706.09141] Causal Structure Learning</title>
    <dc:date>2017-11-10T15:38:13+00:00</dc:date>
    <link>https://arxiv.org/abs/1706.09141</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Graphical models can represent a multivariate distribution in a convenient and accessible form as a graph. Causal models can be viewed as a special class of graphical models that not only represent the distribution of the observed system but also the distributions under external interventions. They hence enable predictions under hypothetical interventions, which is important for decision making. The challenging task of learning causal models from data always relies on some underlying assumptions. We discuss several recently proposed structure learning algorithms and their assumptions, and compare their empirical performance under various scenarios."]]></description>
<dc:subject>to:NB to_read maathuis.marloes causal_discovery statistics to_teach:undergrad-ADA</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:8ab8a94c7ca7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:maathuis.marloes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_discovery"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://journals.sagepub.com/eprint/VThwp5JSFz7eNKF5GkxW/full">
    <title>Community and the Crime Decline: The Causal Effect of Local Nonprofits on Violent CrimeAmerican Sociological Review - Patrick Sharkey, Gerard Torrats-Espinosa, Delaram Takyar, 2017</title>
    <dc:date>2017-11-09T18:55:52+00:00</dc:date>
    <link>http://journals.sagepub.com/eprint/VThwp5JSFz7eNKF5GkxW/full</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Largely overlooked in the theoretical and empirical literature on the crime decline is a long tradition of research in criminology and urban sociology that considers how violence is regulated through informal sources of social control arising from residents and organizations internal to communities. In this article, we incorporate the “systemic” model of community life into debates on the U.S. crime drop, and we focus on the role that local nonprofit organizations played in the national decline of violence from the 1990s to the 2010s. Using longitudinal data and a strategy to account for the endogeneity of nonprofit formation, we estimate the causal effect on violent crime of nonprofits focused on reducing violence and building stronger communities. Drawing on a panel of 264 cities spanning more than 20 years, we estimate that every 10 additional organizations focusing on crime and community life in a city with 100,000 residents leads to a 9 percent reduction in the murder rate, a 6 percent reduction in the violent crime rate, and a 4 percent reduction in the property crime rate."

- Last tag conditional on replication data.
]]></description>
<dc:subject>to:NB causal_inference crime institutions via:rvenkat to_teach:undergrad-ADA</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:2c75e19e4b35/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:crime"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:institutions"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:rvenkat"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.pnas.org/content/early/2017/07/27/1619938114.short">
    <title>Empirical prediction intervals improve energy forecasting</title>
    <dc:date>2017-08-07T22:35:02+00:00</dc:date>
    <link>http://www.pnas.org/content/early/2017/07/27/1619938114.short</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Hundreds of organizations and analysts use energy projections, such as those contained in the US Energy Information Administration (EIA)’s Annual Energy Outlook (AEO), for investment and policy decisions. Retrospective analyses of past AEO projections have shown that observed values can differ from the projection by several hundred percent, and thus a thorough treatment of uncertainty is essential. We evaluate the out-of-sample forecasting performance of several empirical density forecasting methods, using the continuous ranked probability score (CRPS). The analysis confirms that a Gaussian density, estimated on past forecasting errors, gives comparatively accurate uncertainty estimates over a variety of energy quantities in the AEO, in particular outperforming scenario projections provided in the AEO. We report probabilistic uncertainties for 18 core quantities of the AEO 2016 projections. Our work frames how to produce, evaluate, and rank probabilistic forecasts in this setting. We propose a log transformation of forecast errors for price projections and a modified nonparametric empirical density forecasting method. Our findings give guidance on how to evaluate and communicate uncertainty in future energy outlooks."

--- It's probably presumptuous of me, but I am a bit proud, because the first author learned a lot of these methods from my class...]]></description>
<dc:subject>to:NB to_read heard_the_talk energy prediction statistics to_teach:undergrad-ADA</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d475d9942844/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:heard_the_talk"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:energy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://journal.sjdm.org/17/17217/jdm17217.html">
    <title>FFTrees: A toolbox to create, visualize, and evaluate fast-and-frugal decision trees</title>
    <dc:date>2017-08-01T14:47:49+00:00</dc:date>
    <link>http://journal.sjdm.org/17/17217/jdm17217.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Fast-and-frugal trees (FFTs) are simple algorithms that facilitate efficient and accurate decisions based on limited information. But despite their successful use in many applied domains, there is no widely available toolbox that allows anyone to easily create, visualize, and evaluate FFTs. We fill this gap by introducing the R package FFTrees. In this paper, we explain how FFTs work, introduce a new class of algorithms called fan for constructing FFTs, and provide a tutorial for using the FFTrees package. We then conduct a simulation across ten real-world datasets to test how well FFTs created by FFTrees can predict data. Simulation results show that FFTs created by FFTrees can predict data as well as popular classification algorithms such as regression and random forests, while remaining simple enough for anyone to understand and use."

--- I am skeptical about that "simple enough for anyone to understand and use"]]></description>
<dc:subject>decision_trees heuristics cognitive_science R have_read to_teach:undergrad-ADA re:ADAfaEPoV in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:cc94e08f69e8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:decision_trees"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:heuristics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:ADAfaEPoV"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.povertyactionlab.org/evaluations">
    <title>Evaluations | The Abdul Latif Jameel Poverty Action Lab</title>
    <dc:date>2017-06-22T19:15:27+00:00</dc:date>
    <link>https://www.povertyactionlab.org/evaluations</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Search our database of 841 randomized evaluations conducted by our affiliates in 80 countries. To browse summaries of key policy recommendations from a subset of these evaluations, visit the Policy Publications tab."]]></description>
<dc:subject>to:NB causal_inference experimental_economics experimental_sociology statistics re:ADAfaEPoV to_teach:undergrad-ADA economics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b6df87663a52/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:experimental_economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:experimental_sociology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:ADAfaEPoV"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.pnas.org/content/114/25/E4944.abstract">
    <title>Probabilistic model predicts dynamics of vegetation biomass in a desert ecosystem in NW China</title>
    <dc:date>2017-06-20T17:11:48+00:00</dc:date>
    <link>http://www.pnas.org/content/114/25/E4944.abstract</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The temporal dynamics of vegetation biomass are of key importance for evaluating the sustainability of arid and semiarid ecosystems. In these ecosystems, biomass and soil moisture are coupled stochastic variables externally driven, mainly, by the rainfall dynamics. Based on long-term field observations in northwestern (NW) China, we test a recently developed analytical scheme for the description of the leaf biomass dynamics undergoing seasonal cycles with different rainfall characteristics. The probabilistic characterization of such dynamics agrees remarkably well with the field measurements, providing a tool to forecast the changes to be expected in biomass for arid and semiarid ecosystems under climate change conditions. These changes will depend—for each season—on the forecasted rate of rainy days, mean depth of rain in a rainy day, and duration of the season. For the site in NW China, the current scenario of an increase of 10% in rate of rainy days, 10% in mean rain depth in a rainy day, and no change in the season duration leads to forecasted increases in mean leaf biomass near 25% in both seasons."

--- Possible teaching example if data is available?]]></description>
<dc:subject>to:NB ecology to_teach:undergrad-ADA</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:e37e73635722/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ecology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://abandonedfootnotes.blogspot.fr/2016/12/new-book-non-democratic-politics.html">
    <title>Abandoned Footnotes: New Book: Non-Democratic Politics</title>
    <dc:date>2016-12-14T00:27:55+00:00</dc:date>
    <link>https://abandonedfootnotes.blogspot.fr/2016/12/new-book-non-democratic-politics.html</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>books:noted democracy political_science to_teach:undergrad-ADA in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:4cb77e3c1fc7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:democracy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:political_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.aos/1383661266">
    <title>Janzing , Balduzzi , Grosse-Wentrup , Schölkopf : Quantifying causal influences</title>
    <dc:date>2016-12-01T20:16:35+00:00</dc:date>
    <link>http://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.aos/1383661266</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Many methods for causal inference generate directed acyclic graphs (DAGs) that formalize causal relations between n variables. Given the joint distribution on all these variables, the DAG contains all information about how intervening on one variable changes the distribution of the other n−1 variables. However, quantifying the causal influence of one variable on another one remains a nontrivial question.
"Here we propose a set of natural, intuitive postulates that a measure of causal strength should satisfy. We then introduce a communication scenario, where edges in a DAG play the role of channels that can be locally corrupted by interventions. Causal strength is then the relative entropy distance between the old and the new distribution.
"Many other measures of causal strength have been proposed, including average causal effect, transfer entropy, directed information, and information flow. We explain how they fail to satisfy the postulates on simple DAGs of ≤3 nodes. Finally, we investigate the behavior of our measure on time-series, supporting our claims with experiments on simulated data."]]></description>
<dc:subject>to:NB graphical_models time_series causality statistics information_theory to_read re:ADAfaEPoV to_teach:undergrad-ADA</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:cc28ca5ecedf/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:graphical_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:time_series"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:information_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:ADAfaEPoV"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1311.5828">
    <title>[1311.5828] The Splice Bootstrap</title>
    <dc:date>2016-12-01T20:11:16+00:00</dc:date>
    <link>https://arxiv.org/abs/1311.5828</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This paper proposes a new bootstrap method to compute predictive intervals for nonlinear autoregressive time series model forecast. This method we call the splice boobstrap as it involves splicing the last p values of a given series to a suitably simulated series. This ensures that each simulated series will have the same set of p time series values in common, a necessary requirement for computing conditional predictive intervals. Using simulation studies we show the methods gives 90% intervals intervals that are similar to those expected from theory for simple linear and SETAR model driven by normal and non-normal noise. Furthermore, we apply the method to some economic data and demonstrate the intervals compare favourably with cross-validation based intervals."]]></description>
<dc:subject>to:NB bootstrap time_series statistics prediction to_teach:undergrad-ADA re:ADAfaEPoV to_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:dfb04235bd35/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bootstrap"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:time_series"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:ADAfaEPoV"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.economist.com/news/united-states/21710265-local-health-outcomes-predict-trumpward-swings-illness-indicator?fsrc=scn/tw_ec/illness_as_indicator">
    <title>Illness as indicator | The Economist</title>
    <dc:date>2016-11-21T15:03:16+00:00</dc:date>
    <link>http://www.economist.com/news/united-states/21710265-local-health-outcomes-predict-trumpward-swings-illness-indicator?fsrc=scn/tw_ec/illness_as_indicator</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Polling data suggests that on the whole, Mr Trump’s supporters are not particularly down on their luck: within any given level of educational attainment, higher-income respondents are more likely to vote Republican. But what the geographic numbers do show is that the specific subset of Mr Trump’s voters that won him the election—those in counties where he outperformed Mr Romney by large margins—live in communities that are literally dying."

--- Replication files available?]]></description>
<dc:subject>track_down_references us_politics trump.donald whats_gone_wrong_with_america to_teach:undergrad-ADA</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:0ac694af0514/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:track_down_references"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:us_politics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:trump.donald"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:whats_gone_wrong_with_america"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.talyarkoni.org/blog/2016/06/11/the-great-minds-journal-club-discusses-westfall-yarkoni-2016/">
    <title>The Great Minds Journal Club discusses Westfall &amp; Yarkoni (2016) – [citation needed]</title>
    <dc:date>2016-06-22T03:50:44+00:00</dc:date>
    <link>http://www.talyarkoni.org/blog/2016/06/11/the-great-minds-journal-club-discusses-westfall-yarkoni-2016/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[In which Tal Yarkoni pulls off writing a dialogue on his own paper.  (I'd never dare.)]]></description>
<dc:subject>statistics measurement yarkoni.tal to_teach:undergrad-ADA to_teach:linear_models social_measurement causal_inference</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:83c579263aa5/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:measurement"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:yarkoni.tal"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:linear_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_measurement"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://rpubs.com/tslumley/190399">
    <title>RPubs - Non-linear vs non-monotone</title>
    <dc:date>2016-06-21T21:44:47+00:00</dc:date>
    <link>http://rpubs.com/tslumley/190399</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>to_teach:linear_models to_teach:undergrad-ADA regression linear_regression lumley.t.s.</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:882737c193bc/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:linear_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:linear_regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:lumley.t.s."/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0153448">
    <title>PLOS ONE: Trickle-Down Preferences: Preferential Conformity to High Status Peers in Fashion Choices</title>
    <dc:date>2016-05-26T12:58:08+00:00</dc:date>
    <link>http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0153448</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[On first skim, they don't really seem to consider that women who move from low to high status locations are probably _already different_ from those who don't...
I can't believe I'm writing this, but this might really be a job for propensity-score matching.]]></description>
<dc:subject>to:NB social_influence economics shoes re:homophily_and_confounding to_teach:undergrad-ADA color_me_skeptical</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:056d9389ca13/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:shoes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:homophily_and_confounding"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/Quartz/bad-data-guide">
    <title>Quartz/bad-data-guide: An exhaustive reference to problems seen in real-world data along with suggestions on how to resolve them.</title>
    <dc:date>2016-05-02T20:03:53+00:00</dc:date>
    <link>https://github.com/Quartz/bad-data-guide</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[This is pretty good (and not limited to "data journalism").]]></description>
<dc:subject>data_analysis to_teach:undergrad-ADA to_teach:undergrad-research have_read via:?</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a215b6fbe9ee/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
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</item>
<item rdf:about="http://notstatschat.tumblr.com/post/64556449200/barren-proxies">
    <title>Biased and Inefficient - Barren proxies</title>
    <dc:date>2016-04-20T14:44:00+00:00</dc:date>
    <link>http://notstatschat.tumblr.com/post/64556449200/barren-proxies</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>causal_inference graphical_models regression to_teach:undergrad-ADA re:ADAfaEPoV lumley.thomas</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:971be82afe92/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:graphical_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:ADAfaEPoV"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:lumley.thomas"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://bds.sagepub.com/content/2/2/2053951715604334">
    <title>Surfeit and surface | Big Data &amp; Society</title>
    <dc:date>2016-04-19T13:56:27+00:00</dc:date>
    <link>http://bds.sagepub.com/content/2/2/2053951715604334</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[This is awesome.  (But it's also completely compatible with causal inference!)  Also, the cultural references will probably require footnotes in just 10 years.]]></description>
<dc:subject>social_science_methodology sociology data_mining levi.john_martin have_read via:phnk to_teach:undergrad-ADA to_teach:data-mining re:any_p-value_distinguishable_from_zero_is_insufficiently_informative to:blog</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:e9765f81f4df/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:levi.john_martin"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:phnk"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data-mining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:any_p-value_distinguishable_from_zero_is_insufficiently_informative"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:blog"/>
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