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    <title>Pinboard (cshalizi)</title>
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    <description>recent bookmarks from cshalizi</description>
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  </channel><item rdf:about="https://jmlr.org/papers/v23/21-0082.html">
    <title>Rethinking Nonlinear Instrumental Variable Models through Prediction Validity</title>
    <dc:date>2022-07-15T12:26:19+00:00</dc:date>
    <link>https://jmlr.org/papers/v23/21-0082.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Instrumental variables (IV) are widely used in the social and health sciences in situations where a researcher would like to measure a causal effect but cannot perform an experiment. For valid causal inference in an IV model, there must be external (exogenous) variation that (i) has a sufficiently large impact on the variable of interest (called the relevance assumption) and where (ii) the only pathway through which the external variation impacts the outcome is via the variable of interest (called the exclusion restriction). For statistical inference, researchers must also make assumptions about the functional form of the relationship between the three variables. Current practice assumes (i) and (ii) are met, then postulates a functional form with limited input from the data. In this paper, we describe a framework that leverages machine learning to validate these typically unchecked but consequential assumptions in the IV framework, providing the researcher empirical evidence about the quality of the instrument given the data at hand. Central to the proposed approach is the idea of prediction validity. Prediction validity checks that error terms -- which should be independent from the instrument -- cannot be modeled with machine learning any better than a model that is identically zero. We use prediction validity to develop both one-stage and two-stage approaches for IV, and demonstrate their performance on an example relevant to climate change policy."]]></description>
<dc:subject>to_read regression instrumental_variables causal_inference mccormick.tyler rudin.cynthia re:ADAfaEPoV in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:4accdfd21078/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:instrumental_variables"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mccormick.tyler"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:rudin.cynthia"/>
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<item rdf:about="https://arxiv.org/abs/2204.04119">
    <title>[2204.04119] Bespoke Instrumental Variables for Causal Inference</title>
    <dc:date>2022-06-14T13:07:25+00:00</dc:date>
    <link>https://arxiv.org/abs/2204.04119</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Many proposals for the identification of causal effects in the presence of unmeasured confounding require an instrumental variable or negative control that satisfies strong, untestable assumptions. In this paper, we will instead show how one can identify causal effects for a point exposure by using a measured confounder as a 'bespoke instrumental variable'. This strategy requires an external reference population that does not have access to the exposure, and a stability condition on the confounder outcome association between reference and target populations. Building on recent identification results of Richardson and Tchetgen Tchetgen (2021), we develop the semiparametric efficiency theory for a general bespoke instrumental variable model, and obtain a multiply robust locally efficient estimator of the average treatment effect in the treated."

--- Looks cool but I will be missing the talk here (2022-06-15, 11:00--12:00 Baker Hall 232M)]]></description>
<dc:subject>causal_inference instrumental_variables to_read in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f1ca9e948f35/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:instrumental_variables"/>
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<item rdf:about="https://www.nber.org/papers/w29709">
    <title>When is TSLS Actually LATE? | NBER</title>
    <dc:date>2022-02-02T23:00:41+00:00</dc:date>
    <link>https://www.nber.org/papers/w29709</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Linear instrumental variable estimators, such as two-stage least squares (TSLS), are commonly interpreted as estimating positively weighted averages of causal effects, referred to as local average treatment effects (LATEs). We examine whether the LATE interpretation actually applies to the types of TSLS specifications that are used in practice. We show that if the specification includes covariates, which most empirical work does, then the LATE interpretation does not apply in general. Instead, the TSLS estimator will in general reflect treatment effects for both compliers and always/never-takers, and some of the treatment effects for the always/never-takers will necessarily be negatively weighted. We show that the only specifications that have a LATE interpretation are "saturated" specifications that control for covariates nonparametrically, implying that such specifications are both sufficient and necessary for TSLS to have a LATE interpretation, at least without additional parametric assumptions. This result is concerning because, as we document, empirical researchers almost never control for covariates nonparametrically, and rarely discuss or justify parametric specifications of covariates. We develop a decomposition that quantifies the extent to which the usual LATE interpretation fails. We apply the decomposition to four empirical analyses and find strong evidence that the LATE interpretation of TSLS is far from accurate for the types of specifications actually used in practice."]]></description>
<dc:subject>instrumental_variables causal_inference nonparametrics re:ADAfaEPoV in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:4f733f8f89d2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:instrumental_variables"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:nonparametrics"/>
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<item rdf:about="https://arxiv.org/abs/2104.10633">
    <title>[2104.10633] A calculus for causal inference with instrumental variables</title>
    <dc:date>2021-04-22T15:21:03+00:00</dc:date>
    <link>https://arxiv.org/abs/2104.10633</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Under a general structural equation framework for causal inference, we provide a definition of the causal effect of a variable X on another variable Y, and propose an approach to estimate this causal effect via the use of instrumental variables."]]></description>
<dc:subject>instrumental_variables causal_inference to_read re:ADAfaEPoV in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:7414bb55e669/</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"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:ADAfaEPoV"/>
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<item rdf:about="https://arxiv.org/abs/2101.04346">
    <title>[2101.04346] Empirical Decomposition of the IV-OLS Gap with Heterogeneous and Nonlinear Effects</title>
    <dc:date>2021-01-14T16:18:55+00:00</dc:date>
    <link>https://arxiv.org/abs/2101.04346</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This study proposes an econometric framework to interpret and empirically decompose the difference between IV and OLS estimates given by the linear regression equation when the true causal effects of the treatment are nonlinear in treatment levels and heterogeneous across covariates. I show that the IV-OLS coefficient gap consists of three estimable components: the difference in weights on the covariates, the difference in weights on the treatment levels, and the difference in identified marginal effects associated with endogeneity bias. Applications of this framework to return-to-schooling estimates demonstrate the empirical relevance of this distinction in properly interpreting the IV-OLS coefficient gap."]]></description>
<dc:subject>linear_regression instrumental_variables misspecification causal_inference in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:1a1a2301e378/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:linear_regression"/>
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</item>
<item rdf:about="https://arxiv.org/abs/2012.10790">
    <title>[2012.10790] Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem</title>
    <dc:date>2020-12-22T03:56:24+00:00</dc:date>
    <link>https://arxiv.org/abs/2012.10790</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Combining machine learning with econometric analysis is becoming increasingly prevalent in both research and practice. A common empirical strategy involves the application of predictive modeling techniques to 'mine' variables of interest from available data, followed by the inclusion of those variables into an econometric framework, with the objective of estimating causal effects. Recent work highlights that, because the predictions from machine learning models are inevitably imperfect, econometric analyses based on the predicted variables are likely to suffer from bias due to measurement error. We propose a novel approach to mitigate these biases, leveraging the ensemble learning technique known as the random forest. We propose employing random forest not just for prediction, but also for generating instrumental variables to address the measurement error embedded in the prediction. The random forest algorithm performs best when comprised of a set of trees that are individually accurate in their predictions, yet which also make 'different' mistakes, i.e., have weakly correlated prediction errors. A key observation is that these properties are closely related to the relevance and exclusion requirements of valid instrumental variables. We design a data-driven procedure to select tuples of individual trees from a random forest, in which one tree serves as the endogenous covariate and the other trees serve as its instruments. Simulation experiments demonstrate the efficacy of the proposed approach in mitigating estimation biases and its superior performance over three alternative methods for bias correction."]]></description>
<dc:subject>to:NB random_forests instrumental_variables mcfowland.edward_iii kith_and_kin</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:6bec70b930d6/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:instrumental_variables"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mcfowland.edward_iii"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.annualreviews.org/doi/abs/10.1146/annurev-economics-080218-025643">
    <title>Weak Instruments in Instrumental Variables Regression: Theory and Practice | Annual Review of Economics</title>
    <dc:date>2020-11-18T21:42:50+00:00</dc:date>
    <link>https://www.annualreviews.org/doi/abs/10.1146/annurev-economics-080218-025643</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["When instruments are weakly correlated with endogenous regressors, conventional methods for instrumental variables (IV) estimation and inference become unreliable. A large literature in econometrics has developed procedures for detecting weak instruments and constructing robust confidence sets, but many of the results in this literature are limited to settings with independent and homoskedastic data, while data encountered in practice frequently violate these assumptions. We review the literature on weak instruments in linear IV regression with an emphasis on results for nonhomoskedastic (heteroskedastic, serially correlated, or clustered) data. To assess the practical importance of weak instruments, we also report tabulations and simulations based on a survey of papers published in the American Economic Review from 2014 to 2018 that use IV. These results suggest that weak instruments remain an important issue for empirical practice, and that there are simple steps that researchers can take to better handle weak instruments in applications."

]]></description>
<dc:subject>instrumental_variables causal_inference statistics in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ae01beb788ca/</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:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</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"/>
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</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://arxiv.org/abs/1708.05499">
    <title>[1708.05499] Inference for high-dimensional instrumental variables regression</title>
    <dc:date>2020-01-12T22:22:15+00:00</dc:date>
    <link>https://arxiv.org/abs/1708.05499</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This paper concerns statistical inference for the components of a high-dimensional regression parameter despite possible endogeneity of each regressor. Given a first-stage linear model for the endogenous regressors and a second-stage linear model for the dependent variable, we develop a novel adaptation of the parametric one-step update to a generic second-stage estimator. We provide conditions under which the scaled update is asymptotically normal. We then introduce a two-stage Lasso procedure and show that the second-stage Lasso estimator satisfies the aforementioned conditions. Using these results, we construct asymptotically valid confidence intervals for the components of the second-stage regression coefficients. We complement our asymptotic theory with simulation studies, which demonstrate the performance of our method in finite samples."]]></description>
<dc:subject>statistics instrumental_variables causal_inference high-dimensional_statistics in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:253c187abdaa/</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:instrumental_variables"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:high-dimensional_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://academic.oup.com/qje/article/134/4/1793/5492274">
    <title>Food Deserts and the Causes of Nutritional Inequality* | The Quarterly Journal of Economics | Oxford Academic</title>
    <dc:date>2020-01-06T16:46:44+00:00</dc:date>
    <link>https://academic.oup.com/qje/article/134/4/1793/5492274</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We study the causes of “nutritional inequality”: why the wealthy eat more healthfully than the poor in the United States. Exploiting supermarket entry and household moves to healthier neighborhoods, we reject that neighborhood environments contribute meaningfully to nutritional inequality. We then estimate a structural model of grocery demand, using a new instrument exploiting the combination of grocery retail chains’ differing presence across geographic markets with their differing comparative advantages across product groups. Counterfactual simulations show that exposing low-income households to the same products and prices available to high-income households reduces nutritional inequality by only about 10%, while the remaining 90% is driven by differences in demand. These findings counter the argument that policies to increase the supply of healthy groceries could play an important role in reducing nutritional inequality."]]></description>
<dc:subject>to:NB economics causal_inference instrumental_variables spatial_statistics food inequality via:gabriel_rossman</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a2594b08ff9f/</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:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:instrumental_variables"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:spatial_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:food"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:inequality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:gabriel_rossman"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1910.09502">
    <title>[1910.09502] Bounds in continuous instrumental variable models</title>
    <dc:date>2019-10-22T13:43:58+00:00</dc:date>
    <link>https://arxiv.org/abs/1910.09502</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Partial identification approaches have seen a sharp increase in interest in econometrics due to improved flexibility and robustness compared to point-identification approaches. However, formidable computational requirements of existing approaches often offset these undeniable advantages---particularly in general instrumental variable models with continuous variables. This article introduces a computationally tractable method for estimating bounds on functionals of counterfactual distributions in continuous instrumental variable models. Its potential applications include randomized trials with imperfect compliance, the evaluation of social programs and, more generally, simultaneous equations models. The method does not require functional form restrictions a priori, but can incorporate parametric or nonparametric assumptions into the estimation process. It proceeds by solving an infinite dimensional program on the paths of a system of counterfactual stochastic processes in order to obtain the counterfactual bounds. A novel "sampling of paths"- approach provides the practical solution concept and probabilistic approximation guarantees. As a demonstration of its capabilities, the method provides informative nonparametric bounds on household expenditures under the sole assumption that expenditure is continuous, showing that partial identification approaches can yield informative bounds under minimal assumptions. Moreover, it shows that additional monotonicity assumptions lead to considerably tighter bounds, which constitutes a novel assessment of the identificatory strength of such nonparametric assumptions in a unified framework."]]></description>
<dc:subject>causal_inference instrumental_variables statistics partial_identification in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:2100f3ab1d4f/</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:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:partial_identification"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1806.09517">
    <title>[1806.09517] Testability of instrument validity under continuous endogenous variables</title>
    <dc:date>2019-10-22T13:10:36+00:00</dc:date>
    <link>https://arxiv.org/abs/1806.09517</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Instrumental variables have informed the research on causal inference in economics over the last century. Despite their ubiquity and decided usefulness, the current consensus in the literature is that the validity of an instrument cannot be tested, particularly when the treatment is continuous. This note addresses this issue in two ways. As a first contribution, it presents a proof confirming the conjecture in Pearl (1995), showing that the validity of an instrument indeed cannot be tested in the most general case when the endogenous variable is continuous. However, as a second contribution, it shows that already weak restrictions on the instrumental variable model reestablish theoretical testability. Continuity is already enough. Monotonicity introduces further testable implications."]]></description>
<dc:subject>instrumental_variables causal_inference statistics color_me_skeptical in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d75964c948b5/</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:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1909.11414">
    <title>[1909.11414] Inequality is rising where social network segregation interacts with urban topology</title>
    <dc:date>2019-09-26T18:46:20+00:00</dc:date>
    <link>https://arxiv.org/abs/1909.11414</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Social networks amplify inequalities due to fundamental mechanisms of social tie formation such as homophily and triadic closure. These forces sharpen social segregation reflected in network fragmentation. Yet, little is known about what structural factors facilitate fragmentation. In this paper we use big data from a widely-used online social network to demonstrate that there is a significant relationship between social network fragmentation and income inequality in cities and towns. We find that the organization of the physical urban space has a stronger relationship with fragmentation than unequal access to education, political segregation, or the presence of ethnic and religious minorities. Fragmentation of social networks is significantly higher in towns in which residential neighborhoods are divided by physical barriers such as rivers and railroads and are relatively distant from the center of town. Towns in which amenities are spatially concentrated are also typically more socially segregated. These relationships suggest how urban planning may be a useful point of intervention to mitigate inequalities in the long run."

--- How on Earth are those exogenous instruments?  (And, of course, neither a theoretical argument for linear functional forms nor an empirical examination of the goodness of fit of the linear models anywhere.)]]></description>
<dc:subject>to:NB have_skimmed inequality social_networks network_data_analysis instrumental_variables statistics color_me_skeptical</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:5d90fba27e31/</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_skimmed"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:inequality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:network_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:instrumental_variables"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1906.00232">
    <title>[1906.00232] Kernel Instrumental Variable Regression</title>
    <dc:date>2019-09-15T14:36:56+00:00</dc:date>
    <link>https://arxiv.org/abs/1906.00232</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Instrumental variable regression is a strategy for learning causal relationships in observational data. If measurements of input X and output Y are confounded, the causal relationship can nonetheless be identified if an instrumental variable Z is available that influences X directly, but is conditionally independent of Y given X and the unmeasured confounder. The classic two-stage least squares algorithm (2SLS) simplifies the estimation problem by modeling all relationships as linear functions. We propose kernel instrumental variable regression (KIV), a nonparametric generalization of 2SLS, modeling relations among X, Y, and Z as nonlinear functions in reproducing kernel Hilbert spaces (RKHSs). We prove the consistency of KIV under mild assumptions, and derive conditions under which the convergence rate achieves the minimax optimal rate for unconfounded, one-stage RKHS regression. In doing so, we obtain an efficient ratio between training sample sizes used in the algorithm's first and second stages. In experiments, KIV outperforms state of the art alternatives for nonparametric instrumental variable regression. Of independent interest, we provide a more general theory of conditional mean embedding regression in which the RKHS has infinite dimension."]]></description>
<dc:subject>instrumental_variables kernel_estimators regression nonparametrics causal_inference statistics re:ADAfaEPoV have_read in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:166a30723496/</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:kernel_estimators"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:nonparametrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<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: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/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://arxiv.org/abs/1611.03015">
    <title>[1611.03015] Honest confidence sets in nonparametric IV regression and other ill-posed models</title>
    <dc:date>2019-08-16T20:32:29+00:00</dc:date>
    <link>https://arxiv.org/abs/1611.03015</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This paper develops inferential methods for a very general class of ill-posed models in econometrics encompassing the nonparametric instrumental regression, various functional regressions, and the density deconvolution. We focus on uniform confidence sets for the parameter of interest estimated with Tikhonov regularization, as in Darolles, Fan, Florens, and Renault (2011). Since it is impossible to have inferential methods based on the central limit theorem, we develop two alternative approaches relying on the concentration inequality and bootstrap approximations. We show that expected diameters and coverage properties of resulting sets have uniform validity over a large class of models, i.e., constructed confidence sets are honest. Monte Carlo experiments illustrate that introduced confidence sets have reasonable width and coverage properties. Using the U.S. data, we provide uniform confidence sets for Engel curves for various commodities."]]></description>
<dc:subject>confidence_sets nonparametrics instrumental_variables regression causal_inference in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f77cb60dea88/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:confidence_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:nonparametrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:instrumental_variables"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://doi.org/10.1093/qje/qjz015">
    <title>Food Deserts and the Causes of Nutritional Inequality* | The Quarterly Journal of Economics | Oxford Academic</title>
    <dc:date>2019-06-19T14:18:01+00:00</dc:date>
    <link>https://doi.org/10.1093/qje/qjz015</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We study the causes of “nutritional inequality” : why the wealthy eat more healthfully than the poor in the United States. Exploiting supermarket entry and household moves to healthier neighborhoods, we reject that neighborhood environments contribute meaningfully to nutritional inequality. We then estimate a structural model of grocery demand, using a new instrument exploiting the combination of grocery retail chains’ differing presence across geographic markets with their differing comparative advantages across product groups. Counterfactual simulations show that exposing low-income households to the same products and prices available to high-income households reduces nutritional inequality by only about 10 percent, while the remaining 90 percent is driven by differences in demand. These findings counter the argument that policies to increase the supply of healthy groceries could play an important role in reducing nutritional inequality."]]></description>
<dc:subject>to:NB economics inequality food instrumental_variables causal_inference</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:abb87bd333f3/</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:inequality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:food"/>
	<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:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1801.06229">
    <title>[1801.06229] Anchor regression: heterogeneous data meets causality</title>
    <dc:date>2019-06-17T16:43:41+00:00</dc:date>
    <link>https://arxiv.org/abs/1801.06229</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Estimating causal parameters from observational data is notoriously difficult. Popular approaches such as regression adjustment or the instrumental variables approach only work under relatively strong assumptions and are prone to mistakes. Furthermore, causal parameters can exhibit conservative predictive performance which can limit their usefulness in practice. Causal parameters can be written as the solution to a minimax risk problem, where the maximum is taken over a range of interventional (or perturbed) distributions. This motivates anchor regression, a method that makes use of exogeneous variables to solve a relaxation of the "causal" minimax problem. The procedure naturally provides an interpolation between the solution to ordinary least squares and two-stage least squares, but also has predictive guarantees if the instrumental variables assumptions are violated. We derive guarantees of the proposed procedure for predictive performance under perturbations for the population case and for high-dimensional data. An additional characterization of the procedure is given in terms of quantiles: If the data follow a Gaussian distribution, the method minimizes quantiles of the conditional mean squared error. If anchor regression and least squares provide the same answer ("anchor stability"), the relationship between targets and predictors is unconfounded and the coefficients have a causal interpretation. Furthermore, we show under which conditions anchor regression satisfies replicability among different experiments. Anchor regression is shown empirically to improve replicability and protect against distributional shifts"]]></description>
<dc:subject>to:NB statistics causal_inference heard_the_talk peters.jonas buhlmann.peter regression instrumental_variables</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a84147f9c682/</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:heard_the_talk"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:peters.jonas"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:buhlmann.peter"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:instrumental_variables"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1905.10176">
    <title>[1905.10176] Machine Learning Estimation of Heterogeneous Treatment Effects with Instruments</title>
    <dc:date>2019-05-27T15:16:48+00:00</dc:date>
    <link>https://arxiv.org/abs/1905.10176</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We consider the estimation of heterogeneous treatment effects with arbitrary machine learning methods in the presence of unobserved confounders with the aid of a valid instrument. Such settings arise in A/B tests with an intent-to-treat structure, where the experimenter randomizes over which user will receive a recommendation to take an action, and we are interested in the effect of the downstream action. We develop a statistical learning approach to the estimation of heterogeneous effects, reducing the problem to the minimization of an appropriate loss function that depends on a set of auxiliary models (each corresponding to a separate prediction task). The reduction enables the use of all recent algorithmic advances (e.g. neural nets, forests). We show that the estimated effect model is robust to estimation errors in the auxiliary models, by showing that the loss satisfies a Neyman orthogonality criterion. Our approach can be used to estimate projections of the true effect model on simpler hypothesis spaces. When these spaces are parametric, then the parameter estimates are asymptotically normal, which enables construction of confidence sets. We applied our method to estimate the effect of membership on downstream webpage engagement on TripAdvisor, using as an instrument an intent-to-treat A/B test among 4 million TripAdvisor users, where some users received an easier membership sign-up process. We also validate our method on synthetic data and on public datasets for the effects of schooling on income."]]></description>
<dc:subject>instrumental_variables nonparametrics regression causal_inference statistics in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:bf64a3c6266e/</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:nonparametrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<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-statistics-031017-100353">
    <title>Handling Missing Data in Instrumental Variable Methods for Causal Inference | Annual Review of Statistics and Its Application</title>
    <dc:date>2019-05-26T16:55:32+00:00</dc:date>
    <link>https://www.annualreviews.org/doi/abs/10.1146/annurev-statistics-031017-100353</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In instrumental variable studies, missing instrument data are very common. For example, in the Wisconsin Longitudinal Study, one can use genotype data as a Mendelian randomization–style instrument, but this information is often missing when subjects do not contribute saliva samples or when the genotyping platform output is ambiguous. Here we review missing at random assumptions one can use to identify instrumental variable causal effects, and discuss various approaches for estimation and inference. We consider likelihood-based methods, regression and weighting estimators, and doubly robust estimators. The likelihood-based methods yield the most precise inference and are optimal under the model assumptions, while the doubly robust estimators can attain the nonparametric efficiency bound while allowing flexible nonparametric estimation of nuisance functions (e.g., instrument propensity scores). The regression and weighting estimators can sometimes be easiest to describe and implement. Our main contribution is an extensive review of this wide array of estimators under varied missing-at-random assumptions, along with discussion of asymptotic properties and inferential tools. We also implement many of the estimators in an analysis of the Wisconsin Longitudinal Study, to study effects of impaired cognitive functioning on depression."]]></description>
<dc:subject>instrumental_variables missing_data statistics causal_inference kith_and_kin mauro.jacqueline small.dylan in_NB kennedy.edward_h.</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:523d3d7df0fd/</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:missing_data"/>
	<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:kith_and_kin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mauro.jacqueline"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:small.dylan"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kennedy.edward_h."/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.annualreviews.org/doi/abs/10.1146/annurev-economics-101617-041813">
    <title>Identification and Extrapolation of Causal Effects with Instrumental Variables | Annual Review of Economics</title>
    <dc:date>2019-05-26T16:38:52+00:00</dc:date>
    <link>https://www.annualreviews.org/doi/abs/10.1146/annurev-economics-101617-041813</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Instrumental variables (IV) are widely used in economics to address selection on unobservables. Standard IV methods produce estimates of causal effects that are specific to individuals whose behavior can be manipulated by the instrument at hand. In many cases, these individuals are not the same as those who would be induced to treatment by an intervention or policy of interest to the researcher. The average causal effect for the two groups can differ significantly if the effect of the treatment varies systematically with unobserved factors that are correlated with treatment choice. We review the implications of this type of unobserved heterogeneity for the interpretation of standard IV methods and for their relevance to policy evaluation. We argue that making inferences about policy-relevant parameters typically requires extrapolating from the individuals affected by the instrument to the individuals who would be induced to treatment by the policy under consideration. We discuss a variety of alternatives to standard IV methods that can be used to rigorously perform this extrapolation. We show that many of these approaches can be nested as special cases of a general framework that embraces the possibility of partial identification."

--- Memo to self: Read this before revising the IV sections of ADAfaEPoV.
]]></description>
<dc:subject>causal_inference instrumental_variables partial_identification statistics re:ADAfaEPoV to_read in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f6be463f6dbe/</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:partial_identification"/>
	<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_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.tandfonline.com/doi/full/10.1080/01621459.2018.1434530">
    <title>Partial Identification of the Average Treatment Effect Using Instrumental Variables: Review of Methods for Binary Instruments, Treatments, and Outcomes: Journal of the American Statistical Association: Vol 113, No 522</title>
    <dc:date>2019-04-24T22:41:26+00:00</dc:date>
    <link>https://www.tandfonline.com/doi/full/10.1080/01621459.2018.1434530</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Several methods have been proposed for partially or point identifying the average treatment effect (ATE) using instrumental variable (IV) type assumptions. The descriptions of these methods are widespread across the statistical, economic, epidemiologic, and computer science literature, and the connections between the methods have not been readily apparent. In the setting of a binary instrument, treatment, and outcome, we review proposed methods for partial and point identification of the ATE under IV assumptions, express the identification results in a common notation and terminology, and propose a taxonomy that is based on sets of identifying assumptions. We further demonstrate and provide software for the application of these methods to estimate bounds. Supplementary materials for this article are available online."]]></description>
<dc:subject>to:NB instrumental_variables causal_inference nonparametrics statistics re:ADAfaEPoV</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ecee1efb70bb/</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: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:re:ADAfaEPoV"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www-jstor-org.proxy.library.cmu.edu/stable/2999545?seq=2#metadata_info_tab_contents">
    <title>Nonparametric Estimation of Triangular Simultaneous Equations Models on JSTOR</title>
    <dc:date>2019-04-24T22:39:57+00:00</dc:date>
    <link>https://www-jstor-org.proxy.library.cmu.edu/stable/2999545?seq=2#metadata_info_tab_contents</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This paper presents a simple two-step nonparametric estimator for a triangular simultaneous equation model. Our approach employs series approximations that exploit the additive structure of the model. The first step comprises the nonparametric estimation of the reduced form and the corresponding residuals. The second step is the estimation of the primary equation via nonparametric regression with the reduced form residuals included as a regressor. We derive consistency and asymptotic normality results for our estimator, including optimal convergence rates. Finally we present an empirical example, based on the relationship between the hourly wage rate and annual hours worked, which illustrates the utility of our approach."]]></description>
<dc:subject>nonparametrics instrumental_variables causal_inference statistics regression econometrics re:ADAfaEPoV in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:46bcb9e6ddc2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:nonparametrics"/>
	<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:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<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://pubs.aeaweb.org/doi/pdfplus/10.1257/aer.103.3.550">
    <title>Whitney K. Newey, &quot;Nonparametric Instrumental Variables Estimation&quot; (2013)</title>
    <dc:date>2019-04-24T22:39:10+00:00</dc:date>
    <link>https://pubs.aeaweb.org/doi/pdfplus/10.1257/aer.103.3.550</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["n many economic models, objects of interest are functions which satisfy conditional moment restrictions. Economics does not restrict the functional form of these models, motivating nonparametric methods. In this paper we review identification results and describe a simple nonparametric instrumental variables (NPIV) estimator. We also consider a simple method of inference. In addition we show how the ability to uncover nonlinearities with conditional moment restrictions is related to the strength of the instruments. We point to applications where important nonlinearities can be found with NPIV and applications where they cannot."]]></description>
<dc:subject>nonparametrics instrumental_variables regression causal_inference statistics econometrics re:ADAfaEPoV in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:30029a2150f1/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:nonparametrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:instrumental_variables"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<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:re:ADAfaEPoV"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://onlinelibrary.wiley.com/doi/pdf/10.3982/ECTA8662">
    <title>Applied Nonparametric Instrumental Variables Estimation - Horowitz - 2011 - Econometrica - Wiley Online Library</title>
    <dc:date>2019-04-24T22:37:30+00:00</dc:date>
    <link>https://onlinelibrary.wiley.com/doi/pdf/10.3982/ECTA8662</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Instrumental variables are widely used in applied econometrics to achieve identification and carry out estimation and inference in models that contain endogenous explanatory variables. In most applications, the function of interest (e.g., an Engel curve or demand function) is assumed to be known up to finitely many parameters (e.g., a linear model), and instrumental variables are used to identify and estimate these parameters. However, linear and other finite‐dimensional parametric models make strong assumptions about the population being modeled that are rarely if ever justified by economic theory or other a priori reasoning and can lead to seriously erroneous conclusions if they are incorrect. This paper explores what can be learned when the function of interest is identified through an instrumental variable but is not assumed to be known up to finitely many parameters. The paper explains the differences between parametric and nonparametric estimators that are important for applied research, describes an easily implemented nonparametric instrumental variables estimator, and presents empirical examples in which nonparametric methods lead to substantive conclusions that are quite different from those obtained using standard, parametric estimators."]]></description>
<dc:subject>nonparametrics instrumental_variables causal_inference econometrics statistics inverse_problems re:ADAfaEPoV in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:60ce2bd00280/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:nonparametrics"/>
	<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:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:inverse_problems"/>
	<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://onlinelibrary.wiley.com/doi/abs/10.3982/ECTA6539">
    <title>Nonparametric Instrumental Regression - Darolles - 2011 - Econometrica - Wiley Online Library</title>
    <dc:date>2019-04-24T22:35:55+00:00</dc:date>
    <link>https://onlinelibrary.wiley.com/doi/abs/10.3982/ECTA6539</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The focus of this paper is the nonparametric estimation of an instrumental regression function ϕ defined by conditional moment restrictions that stem from a structural econometric model E[Y−ϕ(Z)|W]=0, and involve endogenous variables Y and Z and instruments W. The function ϕ is the solution of an ill‐posed inverse problem and we propose an estimation procedure based on Tikhonov regularization. The paper analyzes identification and overidentification of this model, and presents asymptotic properties of the estimated nonparametric instrumental regression function."]]></description>
<dc:subject>nonparametrics instrumental_variables causal_inference statistics inverse_problems regression econometrics re:ADAfaEPoV in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:18b720537b4e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:nonparametrics"/>
	<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:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:inverse_problems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<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://onlinelibrary.wiley.com/doi/pdf/10.1111/1468-0262.00459">
    <title>Instrumental Variable Estimation of Nonparametric Models - Newey - 2003 - Econometrica - Wiley Online Library</title>
    <dc:date>2019-04-24T22:34:56+00:00</dc:date>
    <link>https://onlinelibrary.wiley.com/doi/pdf/10.1111/1468-0262.00459</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In econometrics there are many occasions where knowledge of the structural relationship among dependent variables is required to answer questions of interest. This paper gives identification and estimation results for nonparametric conditional moment restrictions. We characterize identification of structural functions as completeness of certain conditional distributions, and give sufficient identification conditions for exponential families and discrete variables. We also give a consistent, nonparametric estimator of the structural function. The estimator is nonparametric two‐stage least squares based on series approximation, which overcomes an ill‐posed inverse problem by placing bounds on integrals of higher‐order derivatives."

]]></description>
<dc:subject>instrumental_variables nonparametrics regression causal_inference statistics econometrics in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:6b8664a72e96/</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:nonparametrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<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:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=902821">
    <title>A Note on Parametric and Nonparametric Regression in the Presence of Endogenous Control Variables by Markus Frölich :: SSRN</title>
    <dc:date>2019-04-24T22:23:31+00:00</dc:date>
    <link>https://papers.ssrn.com/sol3/papers.cfm?abstract_id=902821</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This note argues that nonparametric regression not only relaxes functional form assumptions vis-a-vis parametric regression, but that it also permits endogenous control variables. To control for selection bias or to make an exclusion restriction in instrumental variables regression valid, additional control variables are often added to a regression. If any of these control variables is endogenous, OLS or 2SLS would be inconsistent and would require further instrumental variables. Nonparametric approaches are still consistent, though. A few examples are examined and it is found that the asymptotic bias of OLS can indeed be very large."]]></description>
<dc:subject>causal_inference instrumental_variables nonparametrics regression statistics re:ADAfaEPoV in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:3e037d84fbba/</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:nonparametrics"/>
	<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:re:ADAfaEPoV"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://ideas.repec.org/p/mtl/montde/2002-05.html">
    <title>Nonparametric Instrumental Regression</title>
    <dc:date>2019-04-22T16:53:09+00:00</dc:date>
    <link>https://ideas.repec.org/p/mtl/montde/2002-05.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The focus of the paper is the nonparametric estimation of an instrumental regression function P defined by conditional moment restrictions stemming from a structural econometric model : E[Y-P(Z)|W]=0 and involving endogenous variables Y and Z and instruments W. The function P is the solution of an ill-posed inverse problem and we propose an estimation procedure based on Tikhonov regularization. The paper analyses identification and overidentification of this model and presents asymptotic properties of the estimated nonparametric instrumental regression function."

--- Was this ever published?  It definitely seems like the most elegant approach to nonparametric IVs I've seen (French econometricians!).

--- ETA: Yes, in _Econometrica_! [https://doi.org/10.3982/ECTA6539]]]></description>
<dc:subject>have_read regression instrumental_variables nonparametrics inverse_problems causal_inference re:ADAfaEPoV econometrics in_NB statistics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:69c772c89634/</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:regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:instrumental_variables"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:nonparametrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:inverse_problems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:ADAfaEPoV"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
</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="http://www.pnas.org/content/115/22/E4970">
    <title>Genetic instrumental variable regression: Explaining socioeconomic and health outcomes in nonexperimental data | PNAS</title>
    <dc:date>2018-05-31T19:18:56+00:00</dc:date>
    <link>http://www.pnas.org/content/115/22/E4970</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Identifying causal effects in nonexperimental data is an enduring challenge. One proposed solution that recently gained popularity is the idea to use genes as instrumental variables [i.e., Mendelian randomization (MR)]. However, this approach is problematic because many variables of interest are genetically correlated, which implies the possibility that many genes could affect both the exposure and the outcome directly or via unobserved confounding factors. Thus, pleiotropic effects of genes are themselves a source of bias in nonexperimental data that would also undermine the ability of MR to correct for endogeneity bias from nongenetic sources. Here, we propose an alternative approach, genetic instrumental variable (GIV) regression, that provides estimates for the effect of an exposure on an outcome in the presence of pleiotropy. As a valuable byproduct, GIV regression also provides accurate estimates of the chip heritability of the outcome variable. GIV regression uses polygenic scores (PGSs) for the outcome of interest which can be constructed from genome-wide association study (GWAS) results. By splitting the GWAS sample for the outcome into nonoverlapping subsamples, we obtain multiple indicators of the outcome PGSs that can be used as instruments for each other and, in combination with other methods such as sibling fixed effects, can address endogeneity bias from both pleiotropy and the environment. In two empirical applications, we demonstrate that our approach produces reasonable estimates of the chip heritability of educational attainment (EA) and show that standard regression and MR provide upwardly biased estimates of the effect of body height on EA."

--- I don't see how this deals with environmental enodgeneity, but I just skimmed it.  For that matter, I don't really see how it can be a valid instrument.  The classic instrument needs a graphical model like (forgive the ASCII art)
Y <-- X <-- V
 ^-U-^
i.e., there's back-door path linking X and Y, but V saves us because it's an ancestor of Y and the _only_ path to Y goes through X.  If I divide the genome into chunks and make a score I calculate on chunk 1 X, and another score on chunk 2 V, how does V only get to cause Y through first affecting X?]]></description>
<dc:subject>to:NB to_read causal_inference human_genetics instrumental_variables statistics heritability color_me_skeptical</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:70e0b2e98039/</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:human_genetics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:instrumental_variables"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:heritability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
</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://www.aeaweb.org/articles?id=10.1257/aer.20151193">
    <title>Virtual Classrooms: How Online College Courses Affect Student Success</title>
    <dc:date>2017-09-01T15:17:06+00:00</dc:date>
    <link>https://www.aeaweb.org/articles?id=10.1257/aer.20151193</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Online college courses are a rapidly expanding feature of higher education, yet little research identifies their effects relative to traditional in-person classes. Using an instrumental variables approach, we find that taking a course online, instead of in-person, reduces student success and progress in college. Grades are lower both for the course taken online and in future courses. Students are less likely to remain enrolled at the university. These estimates are local average treatment effects for students with access to both online and in-person options; for other students, online classes may be the only option for accessing college-level courses."

--- I will be very curious about their instrument, and whether it's at all plausible.]]></description>
<dc:subject>to:NB education instrumental_variables causal_inference statistics re:ADAfaEPoV</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:eac5885efb0d/</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:education"/>
	<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:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:ADAfaEPoV"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.aeaweb.org/articles?id=10.1257/jep.31.2.107">
    <title>Identification and Asymptotic Approximations: Three Examples of Progress in Econometric Theory</title>
    <dc:date>2017-08-30T13:45:22+00:00</dc:date>
    <link>https://www.aeaweb.org/articles?id=10.1257/jep.31.2.107</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In empirical economics, the size and quality of datasets and computational power has grown substantially, along with the size and complexity of the econometric models and the population parameters of interest. With more and better data, it is natural to expect to be able to answer more subtle questions about population relationships, and to pay more attention to the consequences of misspecification of the model for the empirical conclusions. Much of the recent work in econometrics has emphasized two themes: The first is the fragility of statistical identification. The other, related theme involves the way economists make large-sample approximations to the distributions of estimators and test statistics. I will discuss how these issues of identification and alternative asymptotic approximations have been studied in three research areas: analysis of linear endogenous regressor models with many and/or weak instruments; nonparametric models with endogenous regressors; and estimation of partially identified parameters. These areas offer good examples of the progress that has been made in econometrics."]]></description>
<dc:subject>statistics economics instrumental_variables nonparametrics regression identifiability partial_identification in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f82d8af6a73e/</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:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:instrumental_variables"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:nonparametrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:identifiability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:partial_identification"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://pubsonline.informs.org/doi/abs/10.1287/mnsc.1080.0897">
    <title>Identifying Formal and Informal Influence in Technology Adoption with Network Externalities</title>
    <dc:date>2016-03-31T11:23:08+00:00</dc:date>
    <link>http://pubsonline.informs.org/doi/abs/10.1287/mnsc.1080.0897</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Firms introducing network technologies (whose benefits depend on who installs the technology) need to understand which user characteristics confer the greatest network benefits on other potential adopters. To examine which adopter characteristics matter, I use the introduction of a video-messaging technology in an investment bank. I use data on its 2,118 employees, their adoption decisions, and their 2.4 million subsequent calls. The video-messaging technology can also be used to watch TV. Exogenous shocks to the benefits of watching TV are used to identify the causal (network) externality of one individual user's adoption on others' adoption decisions. I allow this network externality to vary in size with a variety of measures of informal and formal influence. I find that adoption by either managers or workers in “boundary spanner” positions has a large impact on the adoption decisions of employees who wish to communicate with them. Adoption by ordinary workers has a negligible impact. This suggests that firms should target those who derive their informal influence from occupying key boundary-spanning positions in communication networks, in addition to those with sources of formal influence, when launching a new network technology."]]></description>
<dc:subject>to:NB causal_inference instrumental_variables diffusion_of_innovations statistics social_influence social_networks re:homophily_and_confounding via:mcfowland color_me_skeptical</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:19e24e0e6055/</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:instrumental_variables"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:diffusion_of_innovations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:homophily_and_confounding"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:mcfowland"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.le.ac.uk/economics/research/RePEc/lec/leecon/dp14-19.pdf?uol_r=d307e306">
    <title>On the Interpretation of Instrumental Variables in the Presence of Specification Errors</title>
    <dc:date>2015-02-17T17:23:40+00:00</dc:date>
    <link>http://www.le.ac.uk/economics/research/RePEc/lec/leecon/dp14-19.pdf?uol_r=d307e306</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The method of instrumental variables (IV) and the generalized method of moments (GMM), and their applications to the estimation of errors-in-variables and simultaneous equations models in econometrics, require data on a sufficient number of instrumental variables that are both exogenous and relevant. We argue that, in general, such instruments (weak or strong) cannot exist."

--- I think they are too quick to dismiss non-parametric IV; if what one wants is consistent estimates of the partial derivatives at a given point, you _can_ get that by (e.g.) splines or locally linear regression.  Need to think through this in terms of Pearl's graphical definition of IVs.]]></description>
<dc:subject>instrumental_variables misspecification regression linear_regression causal_inference statistics econometrics via:jbdelong 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:5e670302d3ca/</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:misspecification"/>
	<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:causal_inference"/>
	<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:jbdelong"/>
	<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="http://arxiv.org/abs/1410.0163">
    <title>[1410.0163] Instrumental Variables: An Econometrician's Perspective</title>
    <dc:date>2015-01-20T02:39:49+00:00</dc:date>
    <link>http://arxiv.org/abs/1410.0163</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["I review recent work in the statistics literature on instrumental variables methods from an econometrics perspective. I discuss some of the older, economic, applications including supply and demand models and relate them to the recent applications in settings of randomized experiments with noncompliance. I discuss the assumptions underlying instrumental variables methods and in what settings these may be plausible. By providing context to the current applications, a better understanding of the applicability of these methods may arise."]]></description>
<dc:subject>econometrics economics instrumental_variables causal_inference statistics in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:3e76f63303fa/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<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:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.pnas.org/content/111/47/16712.abstract.html?etoc">
    <title>Effects of temperature and precipitation variability on the risk of violence in sub-Saharan Africa, 1980–2012</title>
    <dc:date>2014-11-25T22:50:56+00:00</dc:date>
    <link>http://www.pnas.org/content/111/47/16712.abstract.html?etoc</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Ongoing debates in the academic community and in the public policy arena continue without clear resolution about the significance of global climate change for the risk of increased conflict. Sub-Saharan Africa is generally agreed to be the region most vulnerable to such climate impacts. Using a large database of conflict events and detailed climatological data covering the period 1980–2012, we apply a multilevel modeling technique that allows for a more nuanced understanding of a climate–conflict link than has been seen heretofore. In the aggregate, high temperature extremes are associated with more conflict; however, different types of conflict and different subregions do not show consistent relationship with temperature deviations. Precipitation deviations, both high and low, are generally not significant. The location and timing of violence are influenced less by climate anomalies (temperature or precipitation variations from normal) than by key political, economic, and geographic factors. We find important distinctions in the relationship between temperature extremes and conflict by using multiple methods of analysis and by exploiting our time-series cross-sectional dataset for disaggregated analyses."

- Last tag inspired by the supposed existence of replication R code.]]></description>
<dc:subject>to:NB war violence instrumental_variables social_science_methodology statistics causal_inference hierarchical_statistical_models political_science to_teach:undergrad-ADA</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:98da9e18db34/</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:war"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:violence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:instrumental_variables"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_science_methodology"/>
	<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:hierarchical_statistical_models"/>
	<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:Bag></taxo:topics>
</item>
<item rdf:about="http://growthecon.wordpress.com/2014/11/18/the-skeptics-guide-to-institutions-part-1/">
    <title>The Skeptics Guide to Institutions – Part 1 | The Growth Economics Blog</title>
    <dc:date>2014-11-24T02:09:00+00:00</dc:date>
    <link>http://growthecon.wordpress.com/2014/11/18/the-skeptics-guide-to-institutions-part-1/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>to:NB economics development_economics instrumental_variables institutions to_teach:undergrad-ADA track_down_references to:blog</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:bc748a864d5c/</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:development_economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:instrumental_variables"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:institutions"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:track_down_references"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:blog"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://growthecon.wordpress.com/2014/11/20/the-skeptics-guide-to-institutions-part-2/">
    <title>The Skeptics Guide to Institutions – Part 2 | The Growth Economics Blog</title>
    <dc:date>2014-11-24T02:08:01+00:00</dc:date>
    <link>http://growthecon.wordpress.com/2014/11/20/the-skeptics-guide-to-institutions-part-2/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>to:NB economics development_economics instrumental_variables institutions to_teach:undergrad-ADA track_down_references to:blog</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a7eed1e48eb1/</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:development_economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:instrumental_variables"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:institutions"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:track_down_references"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:blog"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.aeaweb.org/articles.php?doi=10.1257/aer.104.11.3635">
    <title>AER (104,11) p. 3635 - Structural Transformation, the Mismeasurement of Productivity Growth, and the Cost Disease of Services</title>
    <dc:date>2014-10-27T19:21:12+00:00</dc:date>
    <link>https://www.aeaweb.org/articles.php?doi=10.1257/aer.104.11.3635</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["If workers self-select into industries based upon their relative productivity in different tasks, and comparative advantage is aligned with absolute advantage, then the average efficacy of a sector's workforce will be negatively correlated with its employment share. This might explain the difference in the reported productivity growth of contracting goods and expanding services. Instrumenting with defense expenditures, I find the elasticity of worker efficacy with respect to employment shares is substantially negative, albeit imprecisely estimated. The estimates suggest that the view that goods and services have similar productivity growth rates is a plausible alternative characterization of growth in developed economies."

--- How on Earth is that a valid instrument for this question???]]></description>
<dc:subject>to:NB to_read economics economic_growth productivity econometrics instrumental_variables</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:fc3ffaa10995/</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:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economic_growth"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:productivity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:instrumental_variables"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.mitpressjournals.org/doi/abs/10.1162/REST_a_00368#.U3ezNV7Tjuc">
    <title>Printing and Protestants: An Empirical Test of the Role of Printing in the Reformation</title>
    <dc:date>2014-05-17T19:09:40+00:00</dc:date>
    <link>http://www.mitpressjournals.org/doi/abs/10.1162/REST_a_00368#.U3ezNV7Tjuc</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The causes of the Protestant Reformation have long been debated. This paper seeks to revive and econometrically test the theory that the spread of the Reformation is linked to the spread of the printing press. I test this theory by analyzing data on the spread of the press and the Reformation at the city level. An econometric analysis that instruments for omitted variable bias with a city's distance from Mainz, the birthplace of printing, suggests that cities with at least one printing press by 1500 were at minimum 29 percentage points more likely to be Protestant by 1600."

--- I'm sympathetic to the theory, but how on Earth could that be a valid instrument?]]></description>
<dc:subject>to:NB the_printing_press_as_an_agent_of_change early_modern_european_history to_read causal_inference christianity reformation instrumental_variables color_me_skeptical</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:65ff04fc70e8/</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:the_printing_press_as_an_agent_of_change"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:early_modern_european_history"/>
	<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:christianity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:reformation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:instrumental_variables"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1307.6701">
    <title>[1307.6701] Iterative Estimation of Solutions to Noisy Nonlinear Operator Equations in Nonparametric Instrumental Regression</title>
    <dc:date>2013-07-26T15:58:12+00:00</dc:date>
    <link>http://arxiv.org/abs/1307.6701</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This paper discusses the solution of nonlinear integral equations with noisy integral kernels as they appear in nonparametric instrumental regression. We propose a regularized Newton-type iteration and establish convergence and convergence rate results. A particular emphasis is on instrumental regression models where the usual conditional mean assumption is replaced by a stronger independence assumption. We demonstrate for the case of a binary instrument that our approach allows the correct estimation of regression functions which are not identifiable with the standard model. This is illustrated in computed examples with simulated data."]]></description>
<dc:subject>inverse_problems optimization instrumental_variables regression causal_inference statistics econometrics in_NB re:ADAfaEPoV</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:98e61af1813f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:inverse_problems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:instrumental_variables"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<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:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:ADAfaEPoV"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://dx.doi.org/10.1016/S0272-7757(98)00048-X">
    <title>Double trouble: on the value of twins-based estimation of the return to schooling</title>
    <dc:date>2013-05-10T16:23:34+00:00</dc:date>
    <link>http://dx.doi.org/10.1016/S0272-7757(98)00048-X</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Several recent studies, including the Rouse (1999) and Behrman & Rosenzweig (1999) articles in this issue, use the schooling and wage variation between monozygotic twins to estimate the return to schooling. In this overview article, we summarize the results from this literature, and we examine the implications of endogenous determination of which twin goes to school longer and of measuring schooling with (possibly mean-reverting) error. Endogeneity of between- twins schooling variation is strongly suggested by the extensive (mostly non-economic) literature documenting that the between-twins difference in birth weight is correlated with the between-twins differences in both schooling and IQ. We conclude that twins-based estimation is vulnerable to the same sort of inconsistency that afflicts conventional cross- sectional estimation. We argue, however, that, if one starts with the presumption that endogenous schooling induces upward inconsistency in the estimated return to schooling, the new twins-based estimates may complement other approaches to tightening the upper bound on the return to schooling."

--- Open preprint version: http://ssrn.com/abstract=226374]]></description>
<dc:subject>causal_inference twin_studies re:g_paper economics education via:samii have_read instrumental_variables in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ea17cb47aba5/</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:twin_studies"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:g_paper"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:education"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:samii"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:instrumental_variables"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.mitpressjournals.org/doi/abs/10.1162/REST_a_00171">
    <title>Identification With Imperfect Instruments</title>
    <dc:date>2012-08-07T13:09:41+00:00</dc:date>
    <link>http://www.mitpressjournals.org/doi/abs/10.1162/REST_a_00171</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Dealing with endogenous regressors is a central challenge of applied research. The standard solution is to use instrumental variables that are assumed to be uncorrelated with unobservables. We instead allow the instrumental variable to be correlated with the error term, but we assume the correlation between the instrumental variable and the error term has the same sign as the correlation between the endogenous regressor and the error term and that the instrumental variable is less correlated with the error term than is the endogenous regressor. Using these assumptions, we derive analytic bounds for the parameters. We demonstrate that the method can generate useful (set) estimates by using it to estimate demand for differentiated products."

Sounds like partial identification a la Manski.]]></description>
<dc:subject>to_read causal_inference instrumental_variables to_teach:undergrad-ADA statistics in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:42976aa320bf/</dc:identifier>
<taxo:topics><rdf:Bag>	<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:instrumental_variables"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1206.5262">
    <title>[1206.5262] Causal Bounds and Instruments</title>
    <dc:date>2012-06-26T13:56:03+00:00</dc:date>
    <link>http://arxiv.org/abs/1206.5262</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Instrumental variables have proven useful, in particular within the social sciences and economics, for making inference about the causal effect of a random variable, B, on another random variable, C, in the presence of unobserved confounders. In the case where relationships are linear, causal effects can be identified exactly from studying the regression of C on A and the regression of B on A, where A is the instrument. In the more general case, bounds have been developed in the literature for the causal effect of B on C, given observational data on the joint distribution of C, B and A. Using an approach based on the analysis of convex polytopes, we develop bounds for the same causal effect when given data on (C,A) and (B,A) only. The bounds developed are thus in direct analogy to the standard use of instruments in econometrics, but we make no assumption of linearity. Use of the bounds is illustrated for experiments with partial compliance. The bounds are, for example, relevant in genetic epidemiology, where the 'Mendelian instrument' S represents a genotype, and where joint data on all of C, B and A may rarely be available but studies involving pairs of these may be abundant. Other examples of bounding causal effects are considered to show that the method applies to DAGs in general, subject to certain conditions."]]></description>
<dc:subject>to_read causal_inference instrumental_variables graphical_models to_teach:undergrad-ADA in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:85b6ab653133/</dc:identifier>
<taxo:topics><rdf:Bag>	<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:instrumental_variables"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:graphical_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:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/math/0603130">
    <title>[math/0603130] Nonparametric methods for inference in the presence of instrumental variables</title>
    <dc:date>2012-04-14T02:47:49+00:00</dc:date>
    <link>http://arxiv.org/abs/math/0603130</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We suggest two nonparametric approaches, based on kernel methods and orthogonal series to estimating regression functions in the presence of instrumental variables. For the first time in this class of problems, we derive optimal convergence rates, and show that they are attained by particular estimators. In the presence of instrumental variables the relation that identifies the regression function also defines an ill-posed inverse problem, the ``difficulty'' of which depends on eigenvalues of a certain integral operator which is determined by the joint density of endogenous and instrumental variables. We delineate the role played by problem difficulty in determining both the optimal convergence rate and the appropriate choice of smoothing parameter."

--- Published version, Annals of Statistics: https://doi.org/10.1214/009053605000000714]]></description>
<dc:subject>regression statistics instrumental_variables nonparametrics to_teach:undergrad-ADA to_read in_NB causal_inference econometrics inverse_problems re:ADAfaEPoV</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:01f9b1b57fec/</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:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:instrumental_variables"/>
	<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:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_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:inverse_problems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:ADAfaEPoV"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://jmlr.csail.mit.edu/papers/v13/ramsahai12a.html">
    <title>Causal Bounds and Observable Constraints for Non-deterministic Models</title>
    <dc:date>2012-04-03T00:37:57+00:00</dc:date>
    <link>http://jmlr.csail.mit.edu/papers/v13/ramsahai12a.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Conditional independence relations involving latent variables do not necessarily imply observable independences. They may imply inequality constraints on observable parameters and causal bounds, which can be used for falsification and identification. The literature on computing such constraints often involve a deterministic underlying data generating process in a counterfactual framework. If an analyst is ignorant of the nature of the underlying mechanisms then they may wish to use a model which allows the underlying mechanisms to be probabilistic. A method of computation for a weaker model without any determinism is given here and demonstrated for the instrumental variable model, though applicable to other models. The approach is based on the analysis of mappings with convex polytopes in a decision theoretic framework and can be implemented in readily available polyhedral computation software. Well known constraints and bounds are replicated in a probabilistic model and novel ones are computed for instrumental variable models without non-deterministic versions of the randomization, exclusion restriction and monotonicity assumptions respectively."

(From a quick scan, this looks too heavy to actually teach in ADAfaEPoV, but it's so tagged to remind me to include a reference.)]]></description>
<dc:subject>causal_inference partial_identification statistics instrumental_variables to_teach:undergrad-ADA in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b762e625b81e/</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:partial_identification"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<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:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.econ.yale.edu/conference/neudc11/papers/paper_199.pdf">
    <title>Rainfall and Conflict - Heather Sarsons</title>
    <dc:date>2012-03-07T13:04:22+00:00</dc:date>
    <link>http://www.econ.yale.edu/conference/neudc11/papers/paper_199.pdf</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Starting with Miguel, Satyanath, and Sergenti (2004), a large literature has used rainfall variation as an instrument to study the impacts of income shocks on civil war and conflict. These studies argue that in agriculturally-dependent regions, negative rain shocks lower income levels, which in turn incites violence. This identification strategy relies on the assumption that rainfall shocks affect conflict only through their impacts on income. I evaluate this exclusion restriction by identifying districts that are downstream from dams in India. In downstream districts, income is much less sensitive to rainfall fluctuations. However, rain shocks remain equally strong predictors of riot incidence in these districts. These results suggest that rainfall affects rioting through a channel other than income and cast doubt on the conclusion that income shocks incite riots."

Cute.]]></description>
<dc:subject>have_read instrumental_variables causal_inference statistics to_teach:undergrad-ADA sociology to:blog in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:79cf54aa4e6a/</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:instrumental_variables"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<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:sociology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:blog"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.iq.harvard.edu/blog/sss/archives/2012/03/rainfall_not_al.shtml">
    <title>Social Science Statistics Blog: Rainfall: not such a great instrument after all...</title>
    <dc:date>2012-03-07T12:44:30+00:00</dc:date>
    <link>http://www.iq.harvard.edu/blog/sss/archives/2012/03/rainfall_not_al.shtml</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>track_down_references instrumental_variables causal_inference to_teach:undergrad-ADA</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:835aa6a78f27/</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:instrumental_variables"/>
	<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="http://www.mitpressjournals.org/doi/abs/10.1162/REST_a_00139">
    <title>Plausibly Exogenous</title>
    <dc:date>2012-02-01T03:59:15+00:00</dc:date>
    <link>http://www.mitpressjournals.org/doi/abs/10.1162/REST_a_00139</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Instrumental variable (IV) methods are widely used to identify causal effects in models with endogenous explanatory variables. Often the instrument exclusion restriction that underlies the validity of the usual IV inference is suspect; that is, instruments are only plausibly exogenous. We present practical methods for performing inference while relaxing the exclusion restriction. We illustrate the approaches with empirical examples that examine the effect of 401(k) participation on asset accumulation, price elasticity of demand for margarine, and returns to schooling. We find that inference is informative even with a substantial relaxation of the exclusion restriction in two of the three cases."]]></description>
<dc:subject>to_read causal_inference regression statistics economics social_science_methodology instrumental_variables to_teach:undergrad-ADA hansen.christian in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f11335fda596/</dc:identifier>
<taxo:topics><rdf:Bag>	<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:regression"/>
	<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:social_science_methodology"/>
	<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:li rdf:resource="https://pinboard.in/u:cshalizi/t:hansen.christian"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1201.0224">
    <title>[1201.0224] Estimation of Treatment Effects with High-Dimensional Controls</title>
    <dc:date>2012-01-28T17:01:10+00:00</dc:date>
    <link>http://arxiv.org/abs/1201.0224</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We propose methods for inference on the average effect of a treatment on a scalar outcome in the presence of very many controls. Our setting is a partially linear regression model containing the treatment/policy variable and a large number $p$ of controls or series terms, with $p$ that is possibly much larger than the sample size $n$, but where only $s < n$ unknown controls or series terms are needed to approximate the regression function accurately. The latter sparsity condition makes it possible to estimate the entire regression function as well as the average treatment effect by selecting an approximately the right set of controls using Lasso and related methods. We develop estimation and inference methods for the average treatment effect in this setting, proposing a novel "post double selection" method that provides attractive inferential and estimation properties. In our analysis, in order to cover realistic applications, we expressly allow for imperfect selection of the controls and account for the impact of selection errors on estimation and inference. In order to cover typical applications in economics, we employ the selection methods designed to deal with non-Gaussian and heteroscedastic disturbances. We illustrate the use of new methods with numerical simulations and an application to the effect of abortion on crime rates."]]></description>
<dc:subject>to_teach:undergrad-ADA regression causal_inference lasso sparsity econometrics instrumental_variables hansen.christian in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ef934cbe84b4/</dc:identifier>
<taxo:topics><rdf:Bag>	<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:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:lasso"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:sparsity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:instrumental_variables"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:hansen.christian"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1201.0220">
    <title>[1201.0220] Inference for High-Dimensional Sparse Econometric Models</title>
    <dc:date>2012-01-28T17:00:12+00:00</dc:date>
    <link>http://arxiv.org/abs/1201.0220</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This article is about estimation and inference methods for high dimensional sparse (HDS) regression models in econometrics. High dimensional sparse models arise in situations where many regressors (or series terms) are available and the regression function is well-approximated by a parsimonious, yet unknown set of regressors. The latter condition makes it possible to estimate the entire regression function effectively by searching for approximately the right set of regressors. We discuss methods for identifying this set of regressors and estimating their coefficients based on $ell_1$-penalization and describe key theoretical results. In order to capture realistic practical situations, we expressly allow for imperfect selection of regressors and study the impact of this imperfect selection on estimation and inference results. We focus the main part of the article on the use of HDS models and methods in the instrumental variables model and the partially linear model. We present a set of novel inference results for these models and illustrate their use with applications to returns to schooling and growth regression."]]></description>
<dc:subject>regression sparsity instrumental_variables econometrics to_teach:undergrad-ADA lasso hansen.christian in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:0e8ff394be79/</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:sparsity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:instrumental_variables"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:lasso"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:hansen.christian"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.aeaweb.org/articles.php?doi=10.1257/jel.49.4.901">
    <title>Nonlinear Models of Measurement Errors</title>
    <dc:date>2011-12-23T21:21:18+00:00</dc:date>
    <link>http://www.aeaweb.org/articles.php?doi=10.1257/jel.49.4.901</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Measurement errors in economic data are pervasive and nontrivial in size. The presence of measurement errors causes biased and inconsistent parameter estimates and leads to erroneous conclusions to various degrees in economic analysis. While linear errors-in-variables models are usually handled with well-known instrumental variable methods, this article provides an overview of recent research papers that derive estimation methods that provide consistent estimates for nonlinear models with measurement errors. We review models with both classical and nonclassical measurement errors, and with misclassification of discrete variables. For each of the methods surveyed, we describe the key ideas for identification and estimation, and discuss its application whenever it is currently available."  (Not read, reconsider to_teach tag later.)]]></description>
<dc:subject>to:NB statistics latent_variables inference_to_latent_objects instrumental_variables econometrics to_teach:undergrad-ADA</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:37bcef54c81e/</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:latent_variables"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:inference_to_latent_objects"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:instrumental_variables"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://blogs.cornell.edu/indolaysia/2011/12/21/omfg-exogenous-variation-or-can-you-find-good-nails-when-you-find-an-indonesian-politics-hammer/">
    <title>OMFG Exogenous Variation! Or, Can You Find Good Nails When You Find an Indonesian Politics Hammer | Indolaysia</title>
    <dc:date>2011-12-23T16:08:12+00:00</dc:date>
    <link>http://blogs.cornell.edu/indolaysia/2011/12/21/omfg-exogenous-variation-or-can-you-find-good-nails-when-you-find-an-indonesian-politics-hammer/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>indonesia causal_inference political_economy instrumental_variables development_economics social_science_methodology to_teach:undergrad-ADA via:henry_farrell in_NB to:blog</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b5a01dbf0bf2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:indonesia"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:political_economy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:instrumental_variables"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:development_economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_science_methodology"/>
	<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:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:blog"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.princeton.edu/~deaton/downloads/deaton%20instruments%20randomization%20learning%20about%20development%20jel%202010.pdf">
    <title>Instruments, Randomization, and Learning about Development (Deaton, 2010)</title>
    <dc:date>2011-12-23T16:07:17+00:00</dc:date>
    <link>http://www.princeton.edu/~deaton/downloads/deaton%20instruments%20randomization%20learning%20about%20development%20jel%202010.pdf</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["There is currently much debate about the effectiveness of foreign aid and about what kind of projects can engender economic development. There is skepticism about the ability of econometric analysis to resolve these issues or of development agencies to learn from their own experience. In response, there is increasing use in development economics of randomized controlled trials (RCTs) to accumulate credible knowl- edge of what works, without overreliance on questionable theory or statistical meth- ods. When RCTs are not possible, the proponents of these methods advocate quasi- randomization through instrumental variable (IV) techniques or natural experiments. I argue that many of these applications are unlikely to recover quantities that are use- ful for policy or understanding: two key issues are the misunderstanding of exogeneity and the handling of heterogeneity. I illustrate from the literature on aid and growth. Actual randomization faces similar problems as does quasi-randomization, notwith- standing rhetoric to the contrary. I argue that experiments have no special ability to produce more credible knowledge than other methods, and that actual experiments are frequently subject to practical problems that undermine any claims to statisti- cal or epistemic superiority. I illustrate using prominent experiments in development and elsewhere. As with IV methods, RCT-based evaluation of projects, without guid- ance from an understanding of underlying mechanisms, is unlikely to lead to scientific progress in the understanding of economic development. I welcome recent trends in development experimentation away from the evaluation of projects and toward the evaluation of theoretical mechanisms."]]></description>
<dc:subject>causal_inference experimental_economics experimental_sociology economics development_economics social_science_methodology explanation_by_mechanisms to_teach:undergrad-ADA instrumental_variables have_read evisceration in_NB randomization to:blog</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:320de43763cb/</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:experimental_economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:experimental_sociology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:development_economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_science_methodology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:explanation_by_mechanisms"/>
	<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:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:evisceration"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:randomization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:blog"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.thaddunning.com/wp-content/uploads/2010/03/Dunning_PRQ.pdf">
    <title>Improving Causal Inference: Strengths and Limitations of Natural Experiments (Dunning, 2008)</title>
    <dc:date>2011-12-23T16:05:42+00:00</dc:date>
    <link>http://www.thaddunning.com/wp-content/uploads/2010/03/Dunning_PRQ.pdf</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Social scientists increasingly exploit natural experiments in their research. This article surveys recent applications in political science, with the goal of illustrating the inferential advantages provided by this research design. When treat- ment assignment is less than “as if” random, studies may be something less than natural experiments, and familiar threats to valid causal inference in observational settings can arise. The author proposes a continuum of plausibility for natural experiments, defined by the extent to which treatment assignment is plausibly “as if” random, and locates several leading studies along this continuum."]]></description>
<dc:subject>causal_inference social_science_methodology to_teach:undergrad-ADA instrumental_variables in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d8556e556b94/</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:social_science_methodology"/>
	<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:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1109.0961">
    <title>[1109.0961] Adaptive estimation of functionals in nonparametric instrumental regression</title>
    <dc:date>2011-09-11T19:10:31+00:00</dc:date>
    <link>http://arxiv.org/abs/1109.0961</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>statistics estimation nonparametrics instrumental_variables regression in_NB</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d67d94cd3b84/</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:estimation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:nonparametrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:instrumental_variables"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.aeaweb.org/articles.php?doi=10.1257/jel.38.4.827">
    <title>Natural &quot;Natural Experiments&quot; in Economics</title>
    <dc:date>2011-04-19T02:33:27+00:00</dc:date>
    <link>http://www.aeaweb.org/articles.php?doi=10.1257/jel.38.4.827</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Shorter: I am sickened by the weakness of your instruments.
]]></description>
<dc:subject>instrumental_variables causal_inference to_teach:undergrad-ADA have_read economics in_NB</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:746ace6ba90e/</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:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.iq.harvard.edu/blog/sss/archives/2010/10/can_matching_so.shtml">
    <title>Social Science Statistics Blog: Can matching solve endogeneity?</title>
    <dc:date>2010-10-31T20:54:53+00:00</dc:date>
    <link>http://www.iq.harvard.edu/blog/sss/archives/2010/10/can_matching_so.shtml</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[" people who like matching methods ... tend to believe that most confounders can be measured ... and that there aren't a lot of lurking unobservables. ... [P]eople ... who are skeptical of matching ... argue that there will always be problematic unobservables lurking ... [and they] prefer instrumental variables approaches .... [T]he same people who tell me that lurking unobservables are everywhere tend to be fairly comfortable making the ... exclusion restrictions that make IV approaches work. The crazy thing is that just like matching, these assumptions [are] about unobservable causal pathways. The claim that an instrumental variable is valid is the claim that there are no unobserved (or observed) variables linking the instrument to the outcome except through the path of the instrumented variable. ... [P]eople who think that lurking unobservables are everywhere in matching somehow think that all these lurking uobservables go away as soon as you call something an instrument..."
]]></description>
<dc:subject>causal_inference instrumental_variables matching to:blog</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a4154d22245a/</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:matching"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:blog"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://ftp.cs.ucla.edu/pub/stat_ser/r356.pdf">
    <title>On a Class of Bias-Amplifying Covariates that Endanger Effect Estimates</title>
    <dc:date>2009-11-10T01:44:38+00:00</dc:date>
    <link>http://ftp.cs.ucla.edu/pub/stat_ser/r356.pdf</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Those would be _instrumental_ variables.  Implications for the collected scholarly works of S. Levitt left as an exercise for the reader.
]]></description>
<dc:subject>causal_inference regression instrumental_variables pearl.judea</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:5582397bba07/</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:regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:instrumental_variables"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:pearl.judea"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://dx.doi.org/10.1016/j.jeconom.2008.01.002">
    <title>Local polynomial estimation of nonparametric simultaneous equations models (Su and Ullah, 2008)</title>
    <dc:date>2009-03-04T18:33:40+00:00</dc:date>
    <link>http://dx.doi.org/10.1016/j.jeconom.2008.01.002</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Looks cool, though it depends on finding instrumental variables, which I always find sketchy.
]]></description>
<dc:subject>nonparametrics econometrics statistics have_read instrumental_variables in_NB</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:1981dbdfa06d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:nonparametrics"/>
	<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:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:instrumental_variables"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.wright.edu/~tdung/Economist_autism_WSJ.pdf">
    <title>Is an Economist Qualified to Solve Puzzle of Autism?</title>
    <dc:date>2008-03-07T02:39:23+00:00</dc:date>
    <link>http://www.wright.edu/~tdung/Economist_autism_WSJ.pdf</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Unfortunately, that NBER paper I linked to appears to be serious.
]]></description>
<dc:subject>autism statistics economics via:erindanielson causal_inference instrumental_variables to_teach:undergrad-ADA</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f20fc5b59381/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:autism"/>
	<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:via:erindanielson"/>
	<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="http://econpapers.repec.org/paper/nbrnberwo/12632.htm">
    <title>EconPapers: Does Television Cause Autism?</title>
    <dc:date>2008-03-06T21:04:42+00:00</dc:date>
    <link>http://econpapers.repec.org/paper/nbrnberwo/12632.htm</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[This is a joke, right?  Right?  Somebody please tell me this is a joke...

ETA: It's not a joke.  It's now a negative example in ADA.

Ungated version: http://forum.johnson.cornell.edu/faculty/waldman/autism-waldman-nicholson-adilov.pdf]]></description>
<dc:subject>please_give_me_strength autism econometrics statistics linear_regression television via:arthegall causal_inference instrumental_variables to_teach:undergrad-ADA</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:3ff5ffd508da/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:please_give_me_strength"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:autism"/>
	<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:linear_regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:television"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:arthegall"/>
	<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>
</rdf:RDF>