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  </channel><item rdf:about="https://www.cambridge.org/core/journals/philosophy-of-science/article/case-for-time-in-causal-dags/FB8A5FA21249300B5250358B21A3E26D?WT.mc_id=New%2520Cambridge%2520Alert%2520-%2520Articles">
    <title>The Case For Time in Causal DAGs | Philosophy of Science | Cambridge Core</title>
    <dc:date>2026-06-24T12:55:55+00:00</dc:date>
    <link>https://www.cambridge.org/core/journals/philosophy-of-science/article/case-for-time-in-causal-dags/FB8A5FA21249300B5250358B21A3E26D?WT.mc_id=New%2520Cambridge%2520Alert%2520-%2520Articles</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We make the case for incorporating a notion of time into causal directed acyclic graphs (DAGs). We demonstrate that nontemporal causal DAGs are ambiguous and obstruct justification of the acyclicity assumption. Assuming that causes precede effects, causal relationships are relative to the time order, and causal DAGs require temporal qualification. We propose a formalization via composite causal variables that refer to quantities at one or multiple time points. We emphasize that the acyclicity assumption requires different justifications depending on whether the time order allows cycles. We conclude by discussing implications for the interpretation and applicability of DAGs as causal models."]]></description>
<dc:subject>to:NB graphical_models causality philosophy_of_science</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:90a1d2ee3253/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:graphical_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causality"/>
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<item rdf:about="https://arxiv.org/abs/2211.15934">
    <title>[2211.15934] Causal identification for continuous-time stochastic processes</title>
    <dc:date>2025-12-18T04:10:32+00:00</dc:date>
    <link>https://arxiv.org/abs/2211.15934</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Many real-world processes are trajectories that may be regarded as continuous-time "functional data". Examples include patients' biomarker concentrations, environmental pollutant levels, and prices of stocks. Corresponding advances in data collection have yielded near continuous-time measurements, from e.g. physiological monitors, wearable digital devices, and environmental sensors. Statistical methodology for estimating the causal effect of a time-varying treatment, measured discretely in time, is well developed. But discrete-time methods like the g-formula, structural nested models, and marginal structural models do not generalize easily to continuous time, due to the entanglement of uncountably infinite variables. Moreover, researchers have shown that the choice of discretization time scale can seriously affect the quality of causal inferences about the effects of an intervention. In this paper, we establish causal identification results for continuous-time treatment-outcome relationships for general cadlag stochastic processes under continuous-time confounding, through orthogonalization and weighting. We use three concrete running examples to demonstrate the plausibility of our identification assumptions, as well as their connections to the discrete-time g methods literature."]]></description>
<dc:subject>causality stochastic_processes causal_inference martingales in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:96f77e1ff841/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:martingales"/>
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<item rdf:about="https://philpapers.org/rec/KINBCC">
    <title>David Kinney &amp; Tania Lombrozo, Building Compressed Causal Models of the World - PhilPapers</title>
    <dc:date>2024-12-11T19:55:08+00:00</dc:date>
    <link>https://philpapers.org/rec/KINBCC</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["A given causal system can be represented in a variety of ways. How do agents determine which variables to include in their causal representations, and at what level of granularity? Using techniques from Bayesian networks, information theory, and decision theory, we develop a formal theory according to which causal representations reflect a trade-off between compression and informativeness, where the optimal trade-off depends on the decision-theoretic value of information for a given agent in a given context. This theory predicts that, all else being equal, agents prefer causal models that are as compressed as possible. When compression is associated with information loss, however, all else is not equal, and our theory predicts that agents will favor compressed models only when the information they sacrifice is not informative with respect to the agent’s anticipated decisions. We then show, across six studies reported here (N=2,364) and one study reported in the supplemental materials (N=182), that participants’ preferences over causal models are in keeping with the predictions of our theory. Our theory offers a unification of different dimensions of causal evaluation identified within the philosophy of science (proportionality and stability), and contributes to a more general picture of human cognition according to which the capacity to create compressed (causal) representations plays a central role"]]></description>
<dc:subject>to:NB cognitive_science graphical_models causality information_theory</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ebc6286ac206/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:information_theory"/>
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<item rdf:about="https://arxiv.org/abs/2301.04709">
    <title>[2301.04709] Causal Abstraction: A Theoretical Foundation for Mechanistic Interpretability</title>
    <dc:date>2024-12-11T19:52:39+00:00</dc:date>
    <link>https://arxiv.org/abs/2301.04709</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Causal abstraction provides a theoretical foundation for mechanistic interpretability, the field concerned with providing intelligible algorithms that are faithful simplifications of the known, but opaque low-level details of black box AI models. Our contributions are (1) generalizing the theory of causal abstraction from mechanism replacement (i.e., hard and soft interventions) to arbitrary mechanism transformation (i.e., functionals from old mechanisms to new mechanisms), (2) providing a flexible, yet precise formalization for the core concepts of modular features, polysemantic neurons, and graded faithfulness, and (3) unifying a variety of mechanistic interpretability methodologies in the common language of causal abstraction, namely activation and path patching, causal mediation analysis, causal scrubbing, causal tracing, circuit analysis, concept erasure, sparse autoencoders, differential binary masking, distributed alignment search, and activation steering."]]></description>
<dc:subject>to:NB causality neural_networks abstraction</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a98e666921c3/</dc:identifier>
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<item rdf:about="https://madeinamericathebook.wordpress.com/2024/05/16/rooting-around-for-root-causes-can-be-a-big-distraction/">
    <title>Rooting Around for “Root Causes” Can Be a Big Distraction | MADE IN AMERICA</title>
    <dc:date>2024-06-18T17:19:25+00:00</dc:date>
    <link>https://madeinamericathebook.wordpress.com/2024/05/16/rooting-around-for-root-causes-can-be-a-big-distraction/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>public_policy causality sociology have_read via:?</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:3e783e7c5de2/</dc:identifier>
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<item rdf:about="https://arxiv.org/abs/2305.11561">
    <title>[2305.11561] Formalising causal inference in time and frequency on process graphs with latent components</title>
    <dc:date>2023-05-27T16:19:33+00:00</dc:date>
    <link>https://arxiv.org/abs/2305.11561</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["When dealing with time series data, causal inference methods often employ structural vector autoregressive (SVAR) processes to model time-evolving random systems. In this work, we rephrase recursive SVAR processes with possible latent component processes as a linear Structural Causal Model (SCM) of stochastic processes on a simple causal graph, the \emph{process graph}, that models every process as a single node. Using this reformulation, we generalise Wright's well-known path-rule for linear Gaussian SCMs to the newly introduced process SCMs and we express the auto-covariance sequence of an SVAR process by means of a generalised trek-rule. Employing the Fourier-Transformation, we derive compact expressions for causal effects in the frequency domain that allow us to efficiently visualise the causal interactions in a multivariate SVAR process. Finally, we observe that the process graph can be used to formulate graphical criteria for identifying causal effects and to derive algebraic relations with which these frequency domain causal effects can be recovered from the observed spectral density."

--- The phrase "frequency domain causal effects" makes my head hurt.]]></description>
<dc:subject>time_series causality graphical_models fourier_analysis in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:41a08583d9d8/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:graphical_models"/>
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<item rdf:about="https://royalsocietypublishing.org/doi/abs/10.1098/rsta.2016.0338?download=true">
    <title>Coarse-graining as a downward causation mechanism | Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences</title>
    <dc:date>2023-05-02T19:30:20+00:00</dc:date>
    <link>https://royalsocietypublishing.org/doi/abs/10.1098/rsta.2016.0338?download=true</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Downward causation is the controversial idea that ‘higher’ levels of organization can causally influence behaviour at ‘lower’ levels of organization. Here I propose that we can gain traction on downward causation by being operational and examining how adaptive systems identify regularities in evolutionary or learning time and use these regularities to guide behaviour. I suggest that in many adaptive systems components collectively compute their macroscopic worlds through coarse-graining. I further suggest we move from simple feedback to downward causation when components tune behaviour in response to estimates of collectively computed macroscopic properties. I introduce a weak and strong notion of downward causation and discuss the role the strong form plays in the origins of new organizational levels. I illustrate these points with examples from the study of biological and social systems and deep neural networks."]]></description>
<dc:subject>to:NB coarse-graining macro_from_micro causality flack.jessica via:henry_farrell</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:443b9dc47537/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macro_from_micro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:flack.jessica"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:henry_farrell"/>
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<item rdf:about="https://link.springer.com/chapter/10.1007/1-4020-4876-9_5">
    <title>Markov Properties and Quantum Experiments | SpringerLink</title>
    <dc:date>2023-05-01T19:56:32+00:00</dc:date>
    <link>https://link.springer.com/chapter/10.1007/1-4020-4876-9_5</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Ungated: https://www.cmu.edu/dietrich/philosophy/docs/glymour/markovquantum.pdf
]]></description>
<dc:subject>in_NB graphical_models causality quantum_mechanics have_read kith_and_kin glymour.clark cleaning_out_the_filing_cabinet_for_the_first_time_since_2005</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:5056035702c1/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:graphical_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causality"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
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<item rdf:about="https://link.springer.com/article/10.1007/BF00258078">
    <title>Stochastic independence, causal independence, and shieldability | SpringerLink</title>
    <dc:date>2022-06-18T15:15:38+00:00</dc:date>
    <link>https://link.springer.com/article/10.1007/BF00258078</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The aim of the paper is to explicate the concept of causal independence between sets of factors and Reichenbach's screening-off-relation in probabilistic terms along the lines of Suppes' probabilistic theory of causality (1970). The probabilistic concept central to this task is that of conditional stochastic independence. The adequacy of the explication is supported by proving some theorems about the explicata which correspond to our intuitions about the explicanda."

--- 1980!]]></description>
<dc:subject>to:NB causality probability philosophy_of_science via:mraginsky</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:3febf3666381/</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:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:probability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:mraginsky"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2108.03099">
    <title>[2108.03099] Causal Inference Theory with Information Dependency Models</title>
    <dc:date>2022-06-06T12:55:51+00:00</dc:date>
    <link>https://arxiv.org/abs/2108.03099</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Inferring the potential consequences of an unobserved event is a fundamental scientific question. To this end, Pearl's celebrated do-calculus provides a set of inference rules to derive an interventional probability from an observational one. In this framework, the primitive causal relations are encoded as functional dependencies in a Structural Causal Model (SCM), which are generally mapped into a Directed Acyclic Graph (DAG) in the absence of cycles. In this paper, by contrast, we capture causality without reference to graphs or functional dependencies, but with information fields and Witsenhausen's intrinsic model. The three rules of do-calculus reduce to a unique sufficient condition for conditional independence, the topological separation, which presents interesting theoretical and practical advantages over the d-separation. With this unique rule, we can deal with systems that cannot be represented with DAGs, for instance systems with cycles and/or 'spurious' edges. We treat an example that cannot be handled-to the extent of our knowledge-with the tools of the current literature. We also explain why, in the presence of cycles, the theory of causal inference might require different tools, depending on whether the random variables are discrete or continuous."

--- The absence of a citation to Raginsky (2011) [https://arxiv.org/abs/1110.0718] is distinctly suspicious.]]></description>
<dc:subject>to:NB causality graphical_models information_theory color_me_skeptical</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ffb707aff322/</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:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:graphical_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:information_theory"/>
	<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/1611.06221">
    <title>[1611.06221] Foundations of Structural Causal Models with Cycles and Latent Variables</title>
    <dc:date>2021-05-12T18:09:18+00:00</dc:date>
    <link>https://arxiv.org/abs/1611.06221</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Structural causal models (SCMs), also known as (nonparametric) structural equation models (SEMs), are widely used for causal modeling purposes. In particular, acyclic SCMs, also known as recursive SEMs, form a well-studied subclass of SCMs that generalize causal Bayesian networks to allow for latent confounders. In this paper, we investigate SCMs in a more general setting, allowing for the presence of both latent confounders and cycles. We show that in the presence of cycles, many of the convenient properties of acyclic SCMs do not hold in general: they do not always have a solution; they do not always induce unique observational, interventional and counterfactual distributions; a marginalization does not always exist, and if it exists the marginal model does not always respect the latent projection; they do not always satisfy a Markov property; and their graphs are not always consistent with their causal semantics. We prove that for SCMs in general each of these properties does hold under certain solvability conditions. Our work generalizes results for SCMs with cycles that were only known for certain special cases so far. We introduce the class of simple SCMs that extends the class of acyclic SCMs to the cyclic setting, while preserving many of the convenient properties of acyclic SCMs. With this paper we aim to provide the foundations for a general theory of statistical causal modeling with SCMs."]]></description>
<dc:subject>to:NB graphical_models causality peters.jonas</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f3134825315f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:graphical_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:peters.jonas"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.journals.uchicago.edu/doi/abs/10.1086/710020?af=R">
    <title>Etiological Kinds | Philosophy of Science: Vol 88, No 1</title>
    <dc:date>2021-04-14T20:04:46+00:00</dc:date>
    <link>https://www.journals.uchicago.edu/doi/abs/10.1086/710020?af=R</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Kinds that share historical properties are dubbed “historical kinds” or “etiological kinds,” and they have some distinctive features. I will try to characterize etiological kinds in general terms and briefly survey some previous philosophical discussions of these kinds. Then I will take a closer look at a few case studies involving different types of etiological kinds. Finally, I will try to understand the rationale for classifying on the basis of etiology, putting forward reasons for classifying phenomena on the basis of diachronic features, thereby making a provisional case for considering at least some etiological kinds to be natural kinds."

--- I'll be interested to see how the paper distinguishes "etiological kinds" from "historical formations" or "historical entities"...]]></description>
<dc:subject>to:NB causality philosophy_of_science barely-comprehensible_metaphysics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:1f05d0fd0be4/</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:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:barely-comprehensible_metaphysics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://oxford-universitypressscholarship-com.cmu.idm.oclc.org/view/10.1093/acprof:oso/9780198524021.001.0001/acprof-9780198524021?rskey=9UW923&amp;result=952">
    <title>Causal Cognition: A Multidisciplinary Debate - Oxford Scholarship</title>
    <dc:date>2021-01-23T06:39:53+00:00</dc:date>
    <link>https://oxford-universitypressscholarship-com.cmu.idm.oclc.org/view/10.1093/acprof:oso/9780198524021.001.0001/acprof-9780198524021?rskey=9UW923&amp;result=952</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Dan Sperber, David Premack, and Ann James Premack (eds.)
"An understanding of cause-effect relationships is fundamental to the study of cognition. In this book, chapters based on comparative psychology, social psychology, developmental psychology, anthropology, and philosophy present the newest developments in the study of causal cognition and discuss their different perspectives. They reflect on the role and forms of causal knowledge, both in animal and human cognition, on the development of human causal cognition from infancy, and on the relationship between individual and cultural aspects of causal understanding. This book presents an informative, insightful, and interdisciplinary debate."]]></description>
<dc:subject>to:NB causality cognitive_science psychology books:noted</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:505527fff17a/</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:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:psychology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://doi.org/10.1093/oso/9780198840718.001.0001">
    <title>Power and Influence: The Metaphysics of Reductive Explanation - Oxford Scholarship</title>
    <dc:date>2021-01-16T07:00:29+00:00</dc:date>
    <link>https://doi.org/10.1093/oso/9780198840718.001.0001</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This book investigates the metaphysical presuppositions of a common—and very successful—reductive approach to dealing with the complexity of the world. The reductive approach in question is one in which we study the components of a complex system in relative isolation, and use the information so gained to explain or predict the behaviour of the complex whole. So, for example, ecologists explain shifts in species population in terms of interactions between individuals, geneticists explain traits of an organism in terms of interactions between genes, and physicists explain the properties of a gas in terms of collisions between the particles that make up the gas. It is argued that this reductive method makes substantive metaphysical assumptions about the world. In particular, the method assumes the existence of causal powers that manifest ‘causal influence’—a relatively unrecognized ontological category of which forces are a paradigm example. The success of the reductive method, therefore, is an argument for the existence of such causal influence. The book goes on to show that adding causal influence to our ontology gives us the resources to solve some traditional problems in the metaphysics of powers, causation, emergence, laws of nature, and possibly even normative ethics. What results, then, is not just an understanding of the reductive method, but an integrated metaphysical world view that is grounded in a novel ontology of power and influence."]]></description>
<dc:subject>to:NB books:noted philosophy_of_science reductionism causality barely-comprehensible_metaphysics to_download</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:56e549dde2b3/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:reductionism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:barely-comprehensible_metaphysics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_download"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://journals.aps.org/pre/abstract/10.1103/PhysRevE.103.012406">
    <title>Phys. Rev. E 103, 012406 (2021) - Characteristics of the neural coding of causality</title>
    <dc:date>2021-01-14T16:04:20+00:00</dc:date>
    <link>https://journals.aps.org/pre/abstract/10.1103/PhysRevE.103.012406</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["While causality processing is an essential cognitive capacity of the neural system, a systematic understanding of the neural coding of causality is still elusive. We propose a physically fundamental analysis of this issue and demonstrate that the neural dynamics encodes the original causality between external events near homomorphically. The causality coding is memory robust for the amount of historical information and features high precision but low recall. This coding process creates a sparser representation for the external causality. Finally, we propose a statistic characterization for the neural coding mapping from the original causality to the coded causality in neural dynamics."]]></description>
<dc:subject>to:NB causality neural_coding_and_decoding color_me_skeptical</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:2f579d2f51ad/</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:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:neural_coding_and_decoding"/>
	<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/2010.09390">
    <title>[2010.09390] Causal Geometry</title>
    <dc:date>2021-01-12T20:57:02+00:00</dc:date>
    <link>https://arxiv.org/abs/2010.09390</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Information geometry has offered a way to formally study the efficacy of scientific models by quantifying the impact of model parameters on the predicted effects. However, there has been little formal investigation of causation in this framework, despite causal models being a fundamental part of science and explanation. Here we introduce causal geometry, which formalizes not only how outcomes are impacted by parameters, but also how the parameters of a model can be intervened upon. Therefore we introduce a geometric version of "effective information" -- a known measure of the informativeness of a causal relationship. We show that it is given by the matching between the space of effects and the space of interventions, in the form of their geometric congruence. Therefore, given a fixed intervention capability, an effective causal model is one that matches those interventions. This is a consequence of "causal emergence," wherein macroscopic causal relationships may carry more information than "fundamental" microscopic ones. We thus argue that a coarse-grained model may, paradoxically, be more informative than the microscopic one, especially when it better matches the scale of accessible interventions -- as we illustrate on toy examples."]]></description>
<dc:subject>to:NB information_geometry emergence causality color_me_skeptical</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f87cabb88637/</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:information_geometry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:emergence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://press.princeton.edu/books/ebook/9780691221489/scientific-explanation-and-the-causal-structure-of-the-world">
    <title>Scientific Explanation and the Causal Structure of the World | Princeton University Press</title>
    <dc:date>2020-12-22T19:40:34+00:00</dc:date>
    <link>https://press.princeton.edu/books/ebook/9780691221489/scientific-explanation-and-the-causal-structure-of-the-world</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[This is a great book that I should re-read.]]></description>
<dc:subject>in_NB books:recommended causality philosophy_of_science explanation explanation_by_mechanisms salmon.wesley_c. prediction_processes</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:fb696d743c7e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:recommended"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:explanation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:explanation_by_mechanisms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:salmon.wesley_c."/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:prediction_processes"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.journals.uchicago.edu/doi/abs/10.1086/710760">
    <title>Near-Decomposability and the Timescale Relativity of Causal Representations | Philosophy of Science: Vol 87, No 5</title>
    <dc:date>2020-12-17T01:34:45+00:00</dc:date>
    <link>https://www.journals.uchicago.edu/doi/abs/10.1086/710760</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["A common strategy for simplifying complex systems involves partitioning them into subsystems whose behaviors are roughly independent of one another at shorter timescales. Dynamic causal models clarify how doing so reveals a system’s nonequilibrium causal relationships. Here I use these models to elucidate the idealizations and abstractions involved in representing a system at a timescale. The models reveal that key features of causal representations—such as which variables are exogenous—may vary with the timescale at which a system is considered. This has implications for debates regarding which systems can be represented causally."]]></description>
<dc:subject>to:NB reductionism causality philosophy_of_science</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ece85fe3df84/</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:reductionism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://causalai.net/r60.pdf">
    <title>On Pearl’s Hierarchy and the Foundations of Causal Inference</title>
    <dc:date>2020-11-11T15:25:03+00:00</dc:date>
    <link>https://causalai.net/r60.pdf</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Hopefully this will actually explain the hierarchy to me.]]></description>
<dc:subject>to:NB to_read causality causal_inference bareinboim.elias</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:e3f21f95e02f/</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:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bareinboim.elias"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.journals.uchicago.edu/doi/abs/10.1086/428914?mobileUi=0&amp;journalCode=ajs">
    <title>Toward Some Fundamentals of Fundamental Causality: Socioeconomic Status and Health in the Routine Clinic Visit for Diabetes1 | American Journal of Sociology: Vol 110, No 5</title>
    <dc:date>2020-07-28T19:03:14+00:00</dc:date>
    <link>https://www.journals.uchicago.edu/doi/abs/10.1086/428914?mobileUi=0&amp;journalCode=ajs</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The concept of “fundamental causality” has gained increasing attention as a way of understanding the relationship between socioeconomic status (SES) and health outcomes. Using enthnographic data from a comparative study of two diabetes clinics, the authors further develop the fundamental cause concept in three ways. First, they provide an exposition of the constituent claims implied by an assertion of fundamental causality. Second, they show how ethnographic data can be used to explicate such claims by showing some of the mechanisms that might operate to preserve the fundamental relationship in diabetes treatment regimens. Finally, they offer elaborations and refinements of the fundamental cause concept."]]></description>
<dc:subject>to:NB causality epidemiology ethnography freese.jeremy inequality medicine diabetes</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:783d0c1e675a/</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:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:epidemiology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ethnography"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:freese.jeremy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:inequality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:medicine"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:diabetes"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1903.03662">
    <title>[1903.03662] A Potential Outcomes Calculus for Identifying Conditional Path-Specific Effects</title>
    <dc:date>2020-07-28T15:15:20+00:00</dc:date>
    <link>https://arxiv.org/abs/1903.03662</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The do-calculus is a well-known deductive system for deriving connections between interventional and observed distributions, and has been proven complete for a number of important identifiability problems in causal inference. Nevertheless, as it is currently defined, the do-calculus is inapplicable to causal problems that involve complex nested counterfactuals which cannot be expressed in terms of the "do" operator. Such problems include analyses of path-specific effects and dynamic treatment regimes. In this paper we present the potential outcome calculus (po-calculus), a natural generalization of do-calculus for arbitrary potential outcomes. We thereby provide a bridge between identification approaches which have their origins in artificial intelligence and statistics, respectively. We use po-calculus to give a complete identification algorithm for conditional path-specific effects with applications to problems in mediation analysis and algorithmic fairness."]]></description>
<dc:subject>to:NB causality causal_inference graphical_models malinsky.daniel richardson.thomas shpitser.ilya</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:cd3dd9da34d7/</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:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:graphical_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:malinsky.daniel"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:richardson.thomas"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:shpitser.ilya"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://link.springer.com/article/10.1023/A:1009602825894">
    <title>An Axiomatic Characterization of Causal Counterfactuals | SpringerLink</title>
    <dc:date>2020-05-16T17:44:25+00:00</dc:date>
    <link>https://link.springer.com/article/10.1023/A:1009602825894</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This paper studies the causal interpretation of counterfactual sentences using a modifiable structural equation model. It is shown that two properties of counterfactuals, namely, composition and effectiveness, are sound and complete relative to this interpretation, when recursive (i.e., feedback-less) models are considered. Composition and effectiveness also hold in Lewis's closest-world semantics, which implies that for recursive models the causal interpretation imposes no restrictions beyond those embodied in Lewis's framework. A third property, called reversibility, holds in nonrecursive causal models but not in Lewis's closest-world semantics, which implies that Lewis's axioms do not capture some properties of systems with feedback. Causal inferences based on counterfactual analysis are exemplified and compared to those based on graphical models."]]></description>
<dc:subject>to:NB causality graphical_models have_read pearl.judea galles.david</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:63ec3efb9a57/</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:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:graphical_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:pearl.judea"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:galles.david"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://link.springer.com/article/10.1007/s11229-018-02014-7">
    <title>A new proposal how to handle counterexamples to Markov causation à la Cartwright, or: fixing the chemical factory | SpringerLink</title>
    <dc:date>2020-04-17T15:54:19+00:00</dc:date>
    <link>https://link.springer.com/article/10.1007/s11229-018-02014-7</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Cartwright (Synthese 121(1/2):3–27, 1999a; The dappled world, Cambridge University Press, Cambridge, 1999b) attacked the view that causal relations conform to the Markov condition by providing a counterexample in which a common cause does not screen off its effects: the prominent chemical factory. In this paper we suggest a new way to handle counterexamples to Markov causation such as the chemical factory. We argue that Cartwright’s as well as similar scenarios (such as decay processes, EPR/B experiments, or spontaneous macro breaking processes) feature a certain kind of non-causal dependence that kicks in once the common cause occurs. We then develop a representation of this specific kind of non-causal dependence that allows for modeling the problematic scenarios in such a way that the Markov condition is not violated anymore."]]></description>
<dc:subject>to:NB causality graphical_models philosophy_of_science</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:0ff441c15352/</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:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:graphical_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.journals.uchicago.edu/doi/abs/10.1086/684173">
    <title>Causally Interpreting Intersectionality Theory | Philosophy of Science: Vol 83, No 1</title>
    <dc:date>2019-11-07T18:54:17+00:00</dc:date>
    <link>https://www.journals.uchicago.edu/doi/abs/10.1086/684173</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Social scientists report difficulties in drawing out testable predictions from the literature on intersectionality theory. We alleviate that difficulty by showing that some characteristic claims of the intersectionality literature can be interpreted causally. The formalism of graphical causal modeling allows claims about the causal effects of occupying intersecting identity categories to be clearly represented and submitted to empirical testing. After outlining this causal interpretation of intersectional theory, we address some concerns that have been expressed in the literature claiming that membership in demographic categories can have causal effects."]]></description>
<dc:subject>to:NB causality explanation philosophy_of_science racism sexism</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:4be5eaa66df2/</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:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:explanation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:racism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:sexism"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.degruyter.com/view/j/jci.ahead-of-print/jci-2019-0026/jci-2019-0026.xml">
    <title>Sufficient Causes: On Oxygen, Matches, and Fires : Journal of Causal Inference</title>
    <dc:date>2019-10-01T15:24:49+00:00</dc:date>
    <link>https://www.degruyter.com/view/j/jci.ahead-of-print/jci-2019-0026/jci-2019-0026.xml</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We demonstrate how counterfactuals can be used to compute the probability that one event was/is a sufficient cause of another, and how counterfactuals emerge organically from basic scientific knowledge, rather than manipulative experiments. We contrast this demonstration with the potential outcome framework and address the distinction between causes and enablers."]]></description>
<dc:subject>to:NB causality pearl.judea</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:df58ed10f64f/</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:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:pearl.judea"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1908.07301">
    <title>[1908.07301] Causality from the Point of View of Classical Statistics</title>
    <dc:date>2019-08-21T13:10:36+00:00</dc:date>
    <link>https://arxiv.org/abs/1908.07301</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["An attempt is made to provide a clear and concise basis for a statistical approach to causality which subsumes and reconciles the models proposed by J. Pearl, J. Robins, D. Rubin and other authors, and which fits in with classical statistical theory and with methods based on stratification and matching. Proofs of the most important results are given, and a variety of examples considered by the different schools of 'statistical causality' are treated in detail and in a self-contained manner."]]></description>
<dc:subject>to:NB causal_inference causality statistics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:802d6f2e3948/</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:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.annualreviews.org/doi/abs/10.1146/annurev-soc-073117-041140">
    <title>Causality and History: Modes of Causal Investigation in Historical Social Sciences | Annual Review of Sociology</title>
    <dc:date>2019-08-01T13:33:15+00:00</dc:date>
    <link>https://www.annualreviews.org/doi/abs/10.1146/annurev-soc-073117-041140</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Studies at the confluence of history and social science address issues of causation in three ways: morphological, variable-centered, and genetic. These approaches to causal investigation differ with regard to their modi operandi, the types of patterns they look for, their underlying assumptions and the challenges they face. Morphological inquiries elaborate causal arguments by uncovering patterns in the empirical layout of socio-historical phenomena. To this end, these inquiries draw on descriptive techniques of data formalization. Variable-centered studies engage causal issues by investigating patterns of association among empirical categories under the twofold assumption that these categories a priori have explanatory relevance and each category empirically has the same meaning across cases. Genetic analyses ground their causal claims by identifying patterned processes of emergence or production."]]></description>
<dc:subject>to:NB causal_inference causality social_science_methodology barely-comprehensible_metaphysics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d3a187f8ab8c/</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:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_science_methodology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:barely-comprehensible_metaphysics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1907.01672">
    <title>[1907.01672] Causal models on probability spaces</title>
    <dc:date>2019-07-05T14:29:29+00:00</dc:date>
    <link>https://arxiv.org/abs/1907.01672</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We describe the interface between measure theoretic probability and causal inference by constructing causal models on probability spaces within the potential outcomes framework. We find that measure theory provides a precise and instructive language for causality and that consideration of the probability spaces underlying causal models offers clarity into central concepts of causal inference. By closely studying simple, instructive examples, we demonstrate insights into causal effects, causal interactions, matching procedures, and randomization. Additionally, we introduce a simple technique for visualizing causal models on probability spaces that is useful both for generating examples and developing causal intuition. Finally, we provide an axiomatic framework for causality and make initial steps towards a formal theory of general causal models."]]></description>
<dc:subject>to:NB causality probability</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:0584f55d5973/</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:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:probability"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1901.00433">
    <title>[1901.00433] Causal Calculus in the Presence of Cycles, Latent Confounders and Selection Bias</title>
    <dc:date>2019-07-05T14:28:54+00:00</dc:date>
    <link>https://arxiv.org/abs/1901.00433</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We prove the main rules of causal calculus (also called do-calculus) for i/o structural causal models (ioSCMs), a generalization of a recently proposed general class of non-/linear structural causal models that allow for cycles, latent confounders and arbitrary probability distributions. We also generalize adjustment criteria and formulas from the acyclic setting to the general one (i.e. ioSCMs). Such criteria then allow to estimate (conditional) causal effects from observational data that was (partially) gathered under selection bias and cycles. This generalizes the backdoor criterion, the selection-backdoor criterion and extensions of these to arbitrary ioSCMs. Together, our results thus enable causal reasoning in the presence of cycles, latent confounders and selection bias. Finally, we extend the ID algorithm for the identification of causal effects to ioSCMs."]]></description>
<dc:subject>to:NB causal_inference causality graphical_models statistics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d97eb4acad2a/</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:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:graphical_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://ndpr.nd.edu/news/causation-in-science-and-the-methods-of-scientific-discovery/">
    <title>Causation in Science and the Methods of Scientific Discovery // Reviews // Notre Dame Philosophical Reviews // University of Notre Dame</title>
    <dc:date>2019-06-19T21:23:45+00:00</dc:date>
    <link>https://ndpr.nd.edu/news/causation-in-science-and-the-methods-of-scientific-discovery/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Go on, Clark, tell us how you really feel...]]></description>
<dc:subject>book_reviews causality causal_inference philosophy_of_science glymour.clark</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:2eaba136fa35/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:book_reviews"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:glymour.clark"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1904.02995">
    <title>[1904.02995] When is an action caused from within? Quantifying the causal chain leading to actions in simulated agents</title>
    <dc:date>2019-06-17T16:57:05+00:00</dc:date>
    <link>https://arxiv.org/abs/1904.02995</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["An agent's actions can be influenced by external factors through the inputs it receives from the environment, as well as internal factors, such as memories or intrinsic preferences. The extent to which an agent's actions are "caused from within", as opposed to being externally driven, should depend on its sensor capacity as well as environmental demands for memory and context-dependent behavior. Here, we test this hypothesis using simulated agents ("animats"), equipped with small adaptive Markov Brains (MB) that evolve to solve a perceptual-categorization task under conditions varied with regards to the agents' sensor capacity and task difficulty. Using a novel formalism developed to identify and quantify the actual causes of occurrences ("what caused what?") in complex networks, we evaluate the direct causes of the animats' actions. In addition, we extend this framework to trace the causal chain ("causes of causes") leading to an animat's actions back in time, and compare the obtained spatio-temporal causal history across task conditions. We found that measures quantifying the extent to which an animat's actions are caused by internal factors (as opposed to being driven by the environment through its sensors) varied consistently with defining aspects of the task conditions they evolved to thrive in."]]></description>
<dc:subject>causality agent-based_models philosophy_of_mind mijnheer_spinoza_mijnheer_benedictus_spinoza_to_the_courtesy_phone_please in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:90f60a37d0e7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:agent-based_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_mind"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mijnheer_spinoza_mijnheer_benedictus_spinoza_to_the_courtesy_phone_please"/>
	<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/full/10.1146/annurev-criminol-011518-024838">
    <title>The Real Gold Standard: Measuring Counterfactual Worlds That Matter Most to Social Science and Policy | Annual Review of Criminology</title>
    <dc:date>2019-05-26T18:14:31+00:00</dc:date>
    <link>https://www.annualreviews.org/doi/full/10.1146/annurev-criminol-011518-024838</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The randomized experiment has achieved the status of the gold standard for estimating causal effects in criminology and the other social sciences. Although causal identification is indeed important and observational data present numerous challenges to causal inference, we argue that conflating causality with the method used to identify it leads to a cognitive narrowing that diverts attention from what ultimately matters most—the difference between counterfactual worlds that emerge as a consequence of their being subjected to different treatment regimes applied to all eligible population members over a sustained period of time. To address this system-level and long-term challenge, we develop an analytic framework for integrating causality and policy inference that accepts the mandate of causal rigor but is conceptually rather than methodologically driven. We then apply our framework to two substantive areas that have generated high-visibility experimental research and that have considerable policy influence: (a) hot-spots policing and (b) the use of housing vouchers to reduce concentrated disadvantage and thereby crime. After reviewing the research in these two areas in light of our framework, we propose a research path forward and conclude with implications for the interplay of theory, data, and causal understanding in criminology and other social sciences."

]]></description>
<dc:subject>to:NB causal_inference causality social_science_methodology statistics nagin.dan kith_and_kin re:ADAfaEPoV</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ef60cfcfb80f/</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:causality"/>
	<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:nagin.dan"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:ADAfaEPoV"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.degruyter.com/view/j/jci.2019.7.issue-1/jci-2019-2002/jci-2019-2002.xml">
    <title>On the Interpretation of do(x) : Journal of Causal Inference</title>
    <dc:date>2019-05-24T23:53:39+00:00</dc:date>
    <link>https://www.degruyter.com/view/j/jci.2019.7.issue-1/jci-2019-2002/jci-2019-2002.xml</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This paper provides empirical interpretation of the do(x) operator when applied to non-manipulable variables such as race, obesity, or cholesterol level. We view do(x) as an ideal intervention that provides valuable information on the effects of manipulable variables and is thus empirically testable. We draw parallels between this interpretation and ways of enabling machines to learn effects of untried actions from those tried. We end with the conclusion that researchers need not distinguish manipulable from non-manipulable variables; both types are equally eligible to receive the do(x) operator and to produce useful information for decision makers."]]></description>
<dc:subject>to:NB causality pearl.judea re:ADAfaEPoV to_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:74e1b77222a0/</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:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:pearl.judea"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:ADAfaEPoV"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.cambridge.org/9781108476676">
    <title>Time and causality across the sciences</title>
    <dc:date>2019-05-14T17:38:55+00:00</dc:date>
    <link>https://www.cambridge.org/9781108476676</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This book, geared toward academic researchers and graduate students, brings together research on all facets of how time and causality relate across the sciences. Time is fundamental to how we perceive and reason about causes. It lets us immediately rule out the sound of a car crash as its cause. That a cause happens before its effect has been a core, and often unquestioned, part of how we describe causality. Research across disciplines shows that the relationship is much more complex than that. This book explores what that means for both the metaphysics and epistemology of causes - what they are and how we can find them. Across psychology, biology, and the social sciences, common themes emerge, suggesting that time plays a critical role in our understanding. The increasing availability of large time series datasets allows us to ask new questions about causality, necessitating new methods for modeling dynamic systems and incorporating mechanistic information into causal models."]]></description>
<dc:subject>to:NB books:noted causal_inference causality arrow_of_time kleinberg.samantha</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:13d192e82886/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:arrow_of_time"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kleinberg.samantha"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://link.springer.com/article/10.1007/s11229-017-1341-z">
    <title>Intervening on structure | SpringerLink</title>
    <dc:date>2018-04-17T13:04:10+00:00</dc:date>
    <link>https://link.springer.com/article/10.1007/s11229-017-1341-z</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Some explanations appeal to facts about the causal structure of a system in order to shed light on a particular phenomenon; these are explanations which do more than cite the causes X and Y of some state-of-affairs Z, but rather appeal to “macro-level” causal features—for example the fact that A causes B as well as C, or perhaps that D is a strong inhibitor of E—in order to explain Z. Appeals to these kinds of “macro-level” causal features appear in a wide variety of social scientific and biological research; statements about features such as “patriarchy,” “healthcare infrastructure,” and “functioning DNA repair mechanism,” for instance, can be understood as claims about what would be different (with respect to some target phenomenon) in a system with a different causal structure. I suggest interpreting counterfactual questions involving structural features as questions about alternative parameter settings of causal models, and propose an extension of the usual interventionist framework for causal explanation which enables scientists to explore the consequences of interventions on “macro-level” structure."]]></description>
<dc:subject>to:NB causality macro_from_micro</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d7c39d81d24a/</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:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macro_from_micro"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1706.02744">
    <title>[1706.02744] Avoiding Discrimination through Causal Reasoning</title>
    <dc:date>2017-11-14T17:07:47+00:00</dc:date>
    <link>https://arxiv.org/abs/1706.02744</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Recent work on fairness in machine learning has focused on various statistical discrimination criteria and how they trade off. Most of these criteria are observational: They depend only on the joint distribution of predictor, protected attribute, features, and outcome. While convenient to work with, observational criteria have severe inherent limitations that prevent them from resolving matters of fairness conclusively. 
"Going beyond observational criteria, we frame the problem of discrimination based on protected attributes in the language of causal reasoning. This viewpoint shifts attention from "What is the right fairness criterion?" to "What do we want to assume about the causal data generating process?" Through the lens of causality, we make several contributions. First, we crisply articulate why and when observational criteria fail, thus formalizing what was before a matter of opinion. Second, our approach exposes previously ignored subtleties and why they are fundamental to the problem. Finally, we put forward natural causal non-discrimination criteria and develop algorithms that satisfy them."]]></description>
<dc:subject>to_read causality algorithmic_fairness prediction machine_learning janzing.dominik re:ADAfaEPoV via:arsyed to_teach:statistics_of_inequality_and_discrimination in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:25748940e755/</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:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:algorithmic_fairness"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:machine_learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:janzing.dominik"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:ADAfaEPoV"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:arsyed"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:statistics_of_inequality_and_discrimination"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://journals.aps.org/prx/abstract/10.1103/PhysRevX.7.031021">
    <title>Phys. Rev. X 7, 031021 (2017) - Quantum Common Causes and Quantum Causal Models</title>
    <dc:date>2017-08-01T14:58:48+00:00</dc:date>
    <link>https://journals.aps.org/prx/abstract/10.1103/PhysRevX.7.031021</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Reichenbach’s principle asserts that if two observed variables are found to be correlated, then there should be a causal explanation of these correlations. Furthermore, if the explanation is in terms of a common cause, then the conditional probability distribution over the variables given the complete common cause should factorize. The principle is generalized by the formalism of causal models, in which the causal relationships among variables constrain the form of their joint probability distribution. In the quantum case, however, the observed correlations in Bell experiments cannot be explained in the manner Reichenbach’s principle would seem to demand. Motivated by this, we introduce a quantum counterpart to the principle. We demonstrate that under the assumption that quantum dynamics is fundamentally unitary, if a quantum channel with input $A$ and outputs $B$ and $C$ is compatible with $A$ being a complete common cause of $B$ and $C$, then it must factorize in a particular way. Finally, we show how to generalize our quantum version of Reichenbach’s principle to a formalism for quantum causal models and provide examples of how the formalism works."]]></description>
<dc:subject>to:NB causality quantum_mechanics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:7e99cb691502/</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:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:quantum_mechanics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.aos/1383661266">
    <title>Janzing , Balduzzi , Grosse-Wentrup , Schölkopf : Quantifying causal influences</title>
    <dc:date>2016-12-01T20:16:35+00:00</dc:date>
    <link>http://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.aos/1383661266</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Many methods for causal inference generate directed acyclic graphs (DAGs) that formalize causal relations between n variables. Given the joint distribution on all these variables, the DAG contains all information about how intervening on one variable changes the distribution of the other n−1 variables. However, quantifying the causal influence of one variable on another one remains a nontrivial question.
"Here we propose a set of natural, intuitive postulates that a measure of causal strength should satisfy. We then introduce a communication scenario, where edges in a DAG play the role of channels that can be locally corrupted by interventions. Causal strength is then the relative entropy distance between the old and the new distribution.
"Many other measures of causal strength have been proposed, including average causal effect, transfer entropy, directed information, and information flow. We explain how they fail to satisfy the postulates on simple DAGs of ≤3 nodes. Finally, we investigate the behavior of our measure on time-series, supporting our claims with experiments on simulated data."]]></description>
<dc:subject>to:NB graphical_models time_series causality statistics information_theory to_read re:ADAfaEPoV to_teach:undergrad-ADA</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:cc28ca5ecedf/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:graphical_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:time_series"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:information_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:ADAfaEPoV"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://kieranhealy.org/publications/transformativetreat/">
    <title>Transformative Treatments</title>
    <dc:date>2016-07-27T13:29:19+00:00</dc:date>
    <link>https://kieranhealy.org/publications/transformativetreat/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Contemporary social-scientific research seeks to identify specific causal mechanisms for outcomes of theoretical interest. Experiments that randomize populations to treatment and control conditions are the “gold standard” for causal inference. We identify, describe, and analyze the problem posed by transformative treatments. Such treatments radically change treated individuals in a way that creates a mismatch in populations, but this mismatch is not empirically detectable at the level of counterfactual dependence. In such cases, the identification of causal pathways is underdetermined in a previously unrecognized way. Moreover, if the treatment is indeed transformative it breaks the inferential structure of the experimental design. Transformative treatments are not curiosities or “corner cases”, but are plausible mechanisms in a large class of events of theoretical interest, particularly ones where deliberate randomization is impractical and quasi-experimental designs are sought instead. They cast long-running debates about treatment and selection effects in a new light, and raise new methodological challenges."

--- After skimming, I'm left spluttering "but, but, _every_ intervention creates a new population!", so I am probably missing something fundamental, and should do more than just skim.]]></description>
<dc:subject>to:NB causality causal_inference barely-comprehensible_metaphysics healy.kieran have_skimmed</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:2501750bf8f2/</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:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:barely-comprehensible_metaphysics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:healy.kieran"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_skimmed"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://mitpress.mit.edu/books/actual-causality">
    <title>Actual Causality | The MIT Press</title>
    <dc:date>2016-06-02T21:09:05+00:00</dc:date>
    <link>https://mitpress.mit.edu/books/actual-causality</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Causality plays a central role in the way people structure the world; we constantly seek causal explanations for our observations. But what does it even mean that an event C “actually caused” event E? The problem of defining actual causation goes beyond mere philosophical speculation. For example, in many legal arguments, it is precisely what needs to be established in order to determine responsibility. The philosophy literature has been struggling with the problem of defining causality since Hume.
"In this book, Joseph Halpern explores actual causality, and such related notions as degree of responsibility, degree of blame, and causal explanation. The goal is to arrive at a definition of causality that matches our natural language usage and is helpful, for example, to a jury deciding a legal case, a programmer looking for the line of code that cause some software to fail, or an economist trying to determine whether austerity caused a subsequent depression.
"Halpern applies and expands an approach to causality that he and Judea Pearl developed, based on structural equations. He carefully formulates a definition of causality, and building on this, defines degree of responsibility, degree of blame, and causal explanation. He concludes by discussing how these ideas can be applied to such practical problems as accountability and program verification. Technical details are generally confined to the final section of each chapter and can be skipped by non-mathematical readers."]]></description>
<dc:subject>to:NB books:noted causality halpern.joseph_y.</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:2b66d058daf6/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:halpern.joseph_y."/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://smr.sagepub.com/content/27/2/148">
    <title>The Causal Devolution</title>
    <dc:date>2016-04-08T22:51:49+00:00</dc:date>
    <link>http://smr.sagepub.com/content/27/2/148</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This article discusses causal analysis as a paradigm for explanation in sociology. It begins with a detailed analysis of causality statements in Durkheim's Le suicide. It then discusses the history of causality assumptions in sociological writing since the 1930s, with brief remarks about the related discipline of econometrics. The author locates the origins of causal argument in a generation of brilliant and brash young sociologists with a model and a mission and then briefly considers the history of causality concepts in modern philosophy. The article closes with reflections on the problems created for sociology by the presumption that causal accounting is the epitome of explanation within the discipline. It is argued that sociology should spend more effort on (and should better reward) descriptive work."]]></description>
<dc:subject>to:NB have_read causality causal_inference sociology social_science_methodology abbott.andrew</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:8f8ff2446323/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:sociology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_science_methodology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:abbott.andrew"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://ndpr.nd.edu/news/58425-causal-reasoning-in-physics/">
    <title>Causal Reasoning in Physics // Reviews // Notre Dame Philosophical Reviews // University of Notre Dame</title>
    <dc:date>2016-01-15T14:23:02+00:00</dc:date>
    <link>https://ndpr.nd.edu/news/58425-causal-reasoning-in-physics/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>books:noted book_reviews physics reductionism causality philosophy_of_science barely-comprehensible_metaphysics in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a22b701c0ea3/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:book_reviews"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:physics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:reductionism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:barely-comprehensible_metaphysics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1512.07942">
    <title>[1512.07942] Multi-Level Cause-Effect Systems</title>
    <dc:date>2016-01-05T18:51:03+00:00</dc:date>
    <link>http://arxiv.org/abs/1512.07942</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We present a domain-general account of causation that applies to settings in which macro-level causal relations between two systems are of interest, but the relevant causal features are poorly understood and have to be aggregated from vast arrays of micro-measurements. Our approach generalizes that of Chalupka et al. (2015) to the setting in which the macro-level effect is not specified. We formalize the connection between micro- and macro-variables in such situations and provide a coherent framework describing causal relations at multiple levels of analysis. We present an algorithm that discovers macro-variable causes and effects from micro-level measurements obtained from an experiment. We further show how to design experiments to discover macro-variables from observational micro-variable data. Finally, we show that under specific conditions, one can identify multiple levels of causal structure. Throughout the article, we use a simulated neuroscience multi-unit recording experiment to illustrate the ideas and the algorithms."]]></description>
<dc:subject>to:NB to_read causality causal_inference macro_from_micro eberhardt.frederick kith_and_kin re:what_is_a_macrostate</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:28c034bbe5ac/</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:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macro_from_micro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:eberhardt.frederick"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:what_is_a_macrostate"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://link.springer.com/article/10.1007/s11229-014-0503-5">
    <title>The ontological status of shocks and trends in macroeconomics - Springer</title>
    <dc:date>2015-12-14T22:53:31+00:00</dc:date>
    <link>http://link.springer.com/article/10.1007/s11229-014-0503-5</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Modern empirical macroeconomic models, known as structural autoregressions (SVARs) are dynamic models that typically claim to represent a causal order among contemporaneously valued variables and to merely represent non-structural (reduced-form) co-occurence between lagged variables and contemporaneous variables. The strategy is held to meet the minimal requirements for identifying the residual errors in particular equations in the model with independent, though otherwise not directly observable, exogenous causes (“shocks”) that ultimately account for change in the model. In nonstationary models, such shocks accumulate so that variables have discernible trends. Econometricians have conceived of variables that trend in sympathy with each other (so-called “cointegrated variables”) as sharing one or more of these unobserved trends as a common cause. It is possible for estimates of the values of both the otherwise unobservable individual shocks and the otherwise unobservable common trends to be backed-out of cointegrated systems of equations. The issue addressed in this paper is whether and in what circumstances these values can be regarded as observations of real entities rather than merely artifacts of the representation of variables in the model. The issue is related, on the one hand, to practical methodological problems in the use of SVARs for policy analysis—e.g., does it make sense to estimate of shocks or trends in one model and then use them as measures of variables in a conceptually distinct model? The issue is also related to debates in the philosophical analysis of causation—particularly, whether we are entitled, as assumed by the developers of Bayes-net approaches, to rely on the causal Markov condition (a generalization of Reichenbach’s common-cause condition) or whether cointegration generates a practical example of Nancy Cartwright’s “byproducts” objection to the causal Markov condition."]]></description>
<dc:subject>to:NB macroeconomics economics time_series hoover.kevin philosophy_of_science causality causal_inference graphical_models have_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:444500c53937/</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:macroeconomics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:time_series"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:hoover.kevin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:graphical_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.homepages.ucl.ac.uk/~ucgtrbd/uai2015_causal/papers/didelez.pdf">
    <title>Causal Reasoning for Events in Continuous Time: A Decision-Theoretic Approach</title>
    <dc:date>2015-07-16T12:18:16+00:00</dc:date>
    <link>http://www.homepages.ucl.ac.uk/~ucgtrbd/uai2015_causal/papers/didelez.pdf</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The dynamics of events occurring in continu- ous time can be modelled using marked point processes, or multi-state processes. Here, we review and extend the work of Røysland et al. (2015) on causal reasoning with local inde- pendence graphs for marked point processes in the context of survival analysis. We relate the results to the decision-theoretic approach of Dawid & Didelez (2010) using influence diagrams, and present additional identifying conditions."

--- VD suggests, orally, that the key bit here is the Doob-Meyer decomposition, and so the concepts may extend to, e.g., solutions of stochastic differential equations.]]></description>
<dc:subject>time_series point_processes causality causal_inference graphical_models statistics didelez.vanessa stochastic_processes heard_the_talk in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:90bcda1d9c9f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:time_series"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:point_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:graphical_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:didelez.vanessa"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:heard_the_talk"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://bjps.oxfordjournals.org/content/early/2015/01/20/bjps.axu047.short">
    <title>The Relation between Kin and Multilevel Selection: An Approach Using Causal Graphs</title>
    <dc:date>2015-03-10T03:06:12+00:00</dc:date>
    <link>http://bjps.oxfordjournals.org/content/early/2015/01/20/bjps.axu047.short</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Kin selection and multilevel selection are alternative approaches for studying the evolution of social behaviour, the relation between which has long been a source of controversy. Many recent theorists regard the two approaches as ultimately equivalent, on the grounds that gene frequency change can be correctly expressed using either. However, this shows only that the two are formally equivalent, not that they offer equally good causal representations of the evolutionary process. This article articulates the notion of an ‘adequate causal representation’ using causal graphs, and then seeks to identify circumstances under which kin and multilevel selection do and do not satisfy the test of causal adequacy."]]></description>
<dc:subject>to:NB to_read causality graphical_models philosophy_of_science evolutionary_biology levels_of_selection via:nybooks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:5b9b7471957b/</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:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:graphical_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:evolutionary_biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:levels_of_selection"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:nybooks"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1410.0470">
    <title>[1410.0470] ACE Bounds; SEMs with Equilibrium Conditions</title>
    <dc:date>2015-01-20T01:56:09+00:00</dc:date>
    <link>http://arxiv.org/abs/1410.0470</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>to:NB causal_inference causality graphical_models statistics economics econometrics richardson.thomas robins.james_m.</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:87a7b302af00/</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:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:graphical_models"/>
	<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:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:richardson.thomas"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:robins.james_m."/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.routledge.com/books/details/9781841692203/">
    <title>Frontiers of Test Validity Theory: Measurement, Causation, and Meaning (Paperback) - Routledge</title>
    <dc:date>2014-11-21T19:39:42+00:00</dc:date>
    <link>http://www.routledge.com/books/details/9781841692203/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This book examines test validity in the behavioral, social, and educational sciences by exploring three fundamental problems: measurement, causation and meaning. Psychometric and philosophical perspectives receive attention along with unresolved issues. The authors explore how measurement is conceived from both the classical and modern perspectives. The importance of understanding the underlying concepts as well as the practical challenges of test construction and use receive emphasis throughout. The book summarizes the current state of the test validity theory field. Necessary background on test theory and statistics is presented as a conceptual overview where needed.
"Each chapter begins with an overview of key material reviewed in previous chapters, concludes with a list of suggested readings, and features boxes with examples that connect theory to practice. These examples reflect actual situations that occurred in psychology, education, and other disciplines in the US and around the globe, bringing theory to life. Critical thinking questions related to the boxed material engage and challenge readers. A few examples include:
"What is the difference between intelligence and IQ?
"Can people disagree on issues of value but agree on issues of test validity?
"Is it possible to ask the same question in two different languages?
"The first part of the book contrasts theories of measurement as applied to the validity of behavioral science measures.The next part considers causal theories of measurement in relation to alternatives such as behavior domain sampling, and then unpacks the causal approach in terms of alternative theories of causation.The final section explores the meaning and interpretation of test scores as it applies to test validity. Each set of chapters opens with a review of the key theories and literature and concludes with a review of related open questions in test validity theory.
"Researchers, practitioners and policy makers interested in test validity or developing tests appreciate the book's cutting edge review of test validity. The book also serves as a supplement in graduate or advanced undergraduate courses on test validity, psychometrics, testing or measurement taught in psychology, education, sociology, social work, political science, business, criminal justice and other fields. The book does not assume a background in measurement."]]></description>
<dc:subject>to:NB books:noted psychometrics social_measurement statistics causality borsboom.denny</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:34ab8ba2a5a4/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:psychometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_measurement"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:borsboom.denny"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://link.springer.com/article/10.1007/s11229-014-0499-x">
    <title>Hume’s definitions of ‘Cause’: Without idealizations, within the bounds of science - Springer</title>
    <dc:date>2014-10-06T16:57:33+00:00</dc:date>
    <link>http://link.springer.com/article/10.1007/s11229-014-0499-x</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Interpreters have found it exceedingly difficult to understand how Hume could be right in claiming that his two definitions of ‘cause’ are essentially the same. As J. A. Robinson points out, the definitions do not even seem to be extensionally equivalent. Don Garrett offers an influential solution to this interpretative problem, one that attributes to Hume the reliance on an ideal observer. I argue that the theoretical need for an ideal observer stems from an idealized concept of definition, which many interpreters, including Garrett, attribute to Hume. I argue that this idealized concept of definition indeed demands an unlimited or infinite ideal observer. But there is substantial textual evidence indicating that Hume disallows the employment of idealizations in general in the sciences. Thus Hume would reject the idealized concept of definition and its corresponding ideal observer. I then put forward an expert-relative reading of Hume’s definitions of ‘cause’, which also renders both definitions extensionally equivalent. On the expert-relative reading, the meaning of ‘cause’ changes with better observations and experiments, but it also allows Humean definitions to play important roles within our normative practices. Finally, I consider and reject Henry Allison’s argument that idealized definitions and their corresponding infinite minds are necessary for expert reflection on the limitations of current science."]]></description>
<dc:subject>to:NB causality hume.david history_of_ideas philosophy</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:cfdad81693b9/</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:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:hume.david"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history_of_ideas"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://link.springer.com/article/10.1007/s11229-013-0380-3?wt_mc=alerts.TOCjournals">
    <title>Systems without a graphical causal representation - Springer</title>
    <dc:date>2014-06-15T19:19:01+00:00</dc:date>
    <link>http://link.springer.com/article/10.1007/s11229-013-0380-3?wt_mc=alerts.TOCjournals</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["There are simple mechanical systems that elude causal representation. We describe one that cannot be represented in a single directed acyclic graph. Our case suggests limitations on the use of causal graphs for causal inference and makes salient the point that causal relations among variables depend upon details of causal setups, including values of variables."]]></description>
<dc:subject>to:NB graphical_models causality color_me_skeptical</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:4e01bddcae6b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:graphical_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://link.springer.com/article/10.1007/s11229-013-0360-7">
    <title>Modelling mechanisms with causal cycles - Springer</title>
    <dc:date>2014-06-15T19:18:40+00:00</dc:date>
    <link>http://link.springer.com/article/10.1007/s11229-013-0360-7</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Mechanistic philosophy of science views a large part of scientific activity as engaged in modelling mechanisms. While science textbooks tend to offer qualitative models of mechanisms, there is increasing demand for models from which one can draw quantitative predictions and explanations. Casini et al. (Theoria 26(1):5–33, 2011) put forward the Recursive Bayesian Networks (RBN) formalism as well suited to this end. The RBN formalism is an extension of the standard Bayesian net formalism, an extension that allows for modelling the hierarchical nature of mechanisms. Like the standard Bayesian net formalism, it models causal relationships using directed acyclic graphs. Given this appeal to acyclicity, causal cycles pose a prima facie problem for the RBN approach. This paper argues that the problem is a significant one given the ubiquity of causal cycles in mechanisms, but that the problem can be solved by combining two sorts of solution strategy in a judicious way."

--- I am mildly curious to see why _two_ strategies are needed, as opposed to distinguishing X(t) from X(t+1) as different variables in the graphical model (as they are).]]></description>
<dc:subject>to:NB graphical_models causality explanation_by_mechanisms philosophy_of_science</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:c5753a87097a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:graphical_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:explanation_by_mechanisms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://ndpr.nd.edu/news/47288-the-principle-of-common-cause/">
    <title>The Principle of Common Cause // Reviews // Notre Dame Philosophical Reviews // University of Notre Dame</title>
    <dc:date>2014-06-14T14:06:36+00:00</dc:date>
    <link>http://ndpr.nd.edu/news/47288-the-principle-of-common-cause/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Incidentally and embarrassingly, in reading this I got hung up on proving to myself that P(A|C) > P(A|!C) implies P(C|A) > P(C|!A).  There may be a simpler way, but my path was to prove that P(A|C) > P(A|!C) iff P(A,C) > P(A)P(C).

For brevity, set P(C) = q.  Now start from P(A,C) > P(A)P(C):
   P(A,C) > P(A)P(C)                      Hypothesis
   qP(A|C) > q^2 P(A|C) + q(1-q)P(A|!C)   Substitution
   1 > q + (1-q) P(A|!C)/P(A|C)           Divide through by LHS (> 0)
   1-q > (1-q) P(A|!C)/P(A|C)             Subtract q from both sides
   1 > P(A|!C)/P(A|C)                     Divide through by LHS (> 0)
   P(A|C) > P(A|!C)                       Multiply through by denominator (> 0)
Reversing the steps gives the other direction.]]></description>
<dc:subject>book_reviews causality probability causal_inference</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:c0bea03c7bcb/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:book_reviews"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:probability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://philsci-archive.pitt.edu/9618/">
    <title>Can Interventionists be Neo-Russellians? Interventionism, the Open Systems Argument and the Arrow of Entropy - PhilSci-Archive</title>
    <dc:date>2014-06-13T22:19:05+00:00</dc:date>
    <link>http://philsci-archive.pitt.edu/9618/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Several proponents of the interventionist theory of causation have recently argued for a neo-Russellian account of causation. The paper discusses two strategies for interventionists to be neo-Russellians. Firstly, I argue that the open systems argument – the main argument for a neo-Russellian account advocated by interventionists – fails. Secondly, I explore and discuss an alternative for interventionists who wish to be neo-Russellians: the statistical mechanical account. Although the latter account is an attractive alternative, it is argued that interventionists are not able to adopt it straightforwardly. Hence, to be neo-Russellians remains a challenge to interventionists."

--- The obvious path seems to be to drop interventionism.]]></description>
<dc:subject>to:NB causality philosophy_of_science barely-comprehensible_metaphysics kith_and_kin reutlinger.alexander have_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:fb89fdb738cc/</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:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:barely-comprehensible_metaphysics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:reutlinger.alexander"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.jstor.org/discover/10.2307/20075662?uid=3739864&amp;uid=2129&amp;uid=2&amp;uid=70&amp;uid=4&amp;uid=3739256&amp;sid=21103637210507">
    <title>Kullback Causality Measures [JSTOR: Annals of Economics and Statistics / Annales d'Économie et de Statistique, No. 6/7 (Apr. - Sep., 1987), pp. 369-410]</title>
    <dc:date>2014-03-11T20:04:11+00:00</dc:date>
    <link>http://www.jstor.org/discover/10.2307/20075662?uid=3739864&amp;uid=2129&amp;uid=2&amp;uid=70&amp;uid=4&amp;uid=3739256&amp;sid=21103637210507</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In this paper we propose causality measures based on the Kullback Information Criterion. These causality measures are applicable in a general context which contains, as special cases, the stationary autoregressive case, considered by GEWEKE, and qualitative models. Estimators of these measures and test procedures are proposed. The nesting of the hypotheses and the asymptotic independence of the test statistics are carefully studied."]]></description>
<dc:subject>to:NB to_read causality information_theory statistics gourieroux.christian monfort.alain via:mraginsky</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:63d233c17e77/</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:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:information_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:gourieroux.christian"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:monfort.alain"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:mraginsky"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1403.1239">
    <title>[1403.1239] Causal diagrams for interference</title>
    <dc:date>2014-03-08T22:16:47+00:00</dc:date>
    <link>http://arxiv.org/abs/1403.1239</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The term "interference" has been used to describe any setting in which one subject's exposure may affect another subject's outcome. We use causal diagrams to distinguish among three causal mechanisms that give rise to interference. The first causal mechanism by which interference can operate is a direct causal effect of one individual's treatment on another individual's outcome; we call this direct interference. Interference by contagion is present when one individual's outcome may affect the outcomes of other individuals with whom he comes into contact. Then giving treatment to the first individual could have an indirect effect on others through the treated individual's outcome. The third pathway by which interference may operate is allocational interference. Treatment in this case allocates individuals to groups; through interactions within a group, individuals' characteristics may affect one another. In many settings more than one type of interference will be present simultaneously. The causal effects of interest differ according to which types of interference are present, as do the conditions under which causal effects are identifiable. Using causal diagrams for interference, we describe these differences, give criteria for the identification of important causal effects, and discuss applications to infectious diseases."]]></description>
<dc:subject>to:NB causal_inference causality graphical_models ogburn.elizabeth vanderweele.tyler heard_the_talk statistics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:60d3023af2fc/</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:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:graphical_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ogburn.elizabeth"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:vanderweele.tyler"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:heard_the_talk"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/cs/0005030">
    <title>[cs/0005030] Axiomatizing Causal Reasoning</title>
    <dc:date>2014-03-06T20:55:28+00:00</dc:date>
    <link>http://arxiv.org/abs/cs/0005030</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Causal models defined in terms of a collection of equations, as defined by Pearl, are axiomatized here. Axiomatizations are provided for three successively more general classes of causal models: (1) the class of recursive theories (those without feedback), (2) the class of theories where the solutions to the equations are unique, (3) arbitrary theories (where the equations may not have solutions and, if they do, they are not necessarily unique). It is shown that to reason about causality in the most general third class, we must extend the language used by Galles and Pearl. In addition, the complexity of the decision procedures is characterized for all the languages and classes of models considered."

- read a long time ago...]]></description>
<dc:subject>to:NB causality logic halpern.joseph</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f5be8b1b6981/</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:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:logic"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:halpern.joseph"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://dx.doi.org/10.1086/673721">
    <title>Is Race a Cause?</title>
    <dc:date>2014-02-10T22:28:50+00:00</dc:date>
    <link>http://dx.doi.org/10.1086/673721</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Advocates of the counterfactual approach to causal inference argue that race is not a cause, and this despite the fact that it is commonly treated as such by scientists in many disciplines. I object that their argument is unsound since two of its premises are false. I also sketch an argument to the effect that racial discrimination cannot be explained unless one assumes race to be a cause."]]></description>
<dc:subject>to:NB causality race racism philosophy_of_science have_read to_teach:undergrad-ADA</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:af53bd7559d4/</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:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:race"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:racism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<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:Bag></taxo:topics>
</item>
<item rdf:about="http://www.jstor.org/action/showArticleInfo?doi=10.1086%2F674206">
    <title>A Formal Framework for Representing Mechanisms?</title>
    <dc:date>2014-02-10T22:26:22+00:00</dc:date>
    <link>http://www.jstor.org/action/showArticleInfo?doi=10.1086%2F674206</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In this article I tackle the question of how the hierarchical order of mechanisms can be represented within a causal graph framework. I illustrate an answer to this question proposed by Casini, Illari, Russo, and Williamson and provide an example that their formalism does not support two important features of nested mechanisms: (i) a mechanism’s submechanisms are typically causally interacting with other parts of said mechanism, and (ii) intervening in some of a mechanism’s parts should have some influence on the phenomena the mechanism brings about. Finally, I sketch an alternative approach taking (i) and (ii) into account."]]></description>
<dc:subject>to:NB explanation_by_mechanisms philosophy_of_science graphical_models causality to_teach:undergrad-ADA</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:db57a5f7303d/</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:explanation_by_mechanisms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:graphical_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.jstor.org/action/showArticleInfo?doi=10.1086%2F673937">
    <title>When to Expect Violations of Causal Faithfulness and Why It Matters</title>
    <dc:date>2014-02-10T22:18:11+00:00</dc:date>
    <link>http://www.jstor.org/action/showArticleInfo?doi=10.1086%2F673937</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["I present three reasons why philosophers of science should be more concerned about violations of causal faithfulness (CF). In complex evolved systems, mechanisms for maintaining equilibrium states are highly likely to violate CF. Even when such systems do not precisely violate CF, they may nevertheless generate precisely the same problems for inferring causal structure from probabilistic relationships in data as do genuine CF violations. Thus, potential CF violations are particularly germane to experimental science when we rely on probabilistic information to uncover causal structures since we cannot then use those structures to predict the right experiments to ‘catch out’ hidden causal relationships."]]></description>
<dc:subject>to:NB causal_inference causality complexity</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:6b72e8ea0514/</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:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:complexity"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://journals.lww.com/epidem/Fulltext/2014/01000/Commentary___Selection_Bias_as_an_Explanation_for.3.aspx">
    <title>Commentary: Selection Bias as an Explanation for the Obesit... : Epidemiology</title>
    <dc:date>2014-01-16T00:01:14+00:00</dc:date>
    <link>http://journals.lww.com/epidem/Fulltext/2014/01000/Commentary___Selection_Bias_as_an_Explanation_for.3.aspx</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>graphical_models causality causal_inference epidemiology obesity to_read to_teach:undergrad-ADA</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:12da166a319e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:graphical_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:epidemiology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:obesity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://onlinelibrary.wiley.com/doi/10.1111/cogs.2013.37.issue-6/issuetoc">
    <title>Cognitive Science - Volume 37, Issue 6 - 2011 Rumelhart Prize Special Issue Honoring Judea Pearl Edited by Steven A. Sloman and Judea Pearl - Wiley Online Library</title>
    <dc:date>2013-12-19T14:58:25+00:00</dc:date>
    <link>http://onlinelibrary.wiley.com/doi/10.1111/cogs.2013.37.issue-6/issuetoc</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>to:NB cognitive_science causal_inference causality graphical_models pearl.judea</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:378a1b548175/</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:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:graphical_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:pearl.judea"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1311.7513">
    <title>[1311.7513] From Statistical Evidence to Evidence of Causality</title>
    <dc:date>2013-12-16T16:21:21+00:00</dc:date>
    <link>http://arxiv.org/abs/1311.7513</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Science is largely concerned with understanding the "effects of causes" (EoC), while Law is more concerned with understanding the "causes of effects" (CoE). While EoC can be addressed using experimental design and statistical analysis, it is less clear how to incorporate statistical or epidemiological evidence into CoE reasoning, as might be required for a case at Law. Some form of counterfactual reasoning, such as Rubin's "potential outcomes" approach, appears unavoidable, but this typically yields "answers" that are sensitive to arbitrary and untestable assumptions. We must therefore recognise that a CoE question simply might not have a well-determined answer. It may nevertheless be possible to use statistical data to set bounds within any answer must lie. With less than perfect data these bounds will themselves be uncertain, leading to a novel compounding of two kinds of uncertainty. Still further care is required in the presence of possible confounding factors. In addition, even identifying the relevant "counterfactual contrast" may be a matter of Policy as much as of Science. Defining the question is as non-trivial a task as finding a route towards an answer. 
"This paper develops some technical elaborations of these philosophical points, and illustrates them with an analysis of a case study in child protection."]]></description>
<dc:subject>to:NB to_read causality causal_inference kith_and_kin to_teach:undergrad-ADA fienberg.stephen_e. dawid.a._philip</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:41a55a5c2a03/</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:causality"/>
	<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:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:fienberg.stephen_e."/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:dawid.a._philip"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://biostats.bepress.com/harvardbiostat/paper163/">
    <title>&quot;On the causal interpretation of race in regressions adjusting for conf&quot; by Tyler J. VanderWeele and Whitney Robinson</title>
    <dc:date>2013-11-22T18:13:26+00:00</dc:date>
    <link>http://biostats.bepress.com/harvardbiostat/paper163/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We consider different possible interpretations of the “effect of race” when regressions are run with race as an exposure variable, controlling also for various confounding and mediating variables. When adjustment is made for socioeconomic status early in a person's life, we discuss under what contexts the regression coefficients for race can be interpreted as corresponding to the extent to which a racial disparity would remain if various socioeconomic distributions early in life across racial groups could be equalized. When adjustment is also made for adult socioeconomic status, we note how the overall disparity can be decomposed into the portion that would be eliminated by equalizing adult socioeconomic status across racial groups and the portion of the disparity that would remain even if adult socioeconomic status across racial groups were equalized. We also discuss a stronger interpretation of the “effect of race” involving the joint effects of skin color, parental skin color, genetic background and cultural context when such variables are thought to be hypothetically manipulable and if adequate control for confounding were possible. We discuss some of the challenges with such an interpretation. Further discussion is given as to how the use of selected populations in examining racial disparities can additionally complicate the interpretation of the effects."]]></description>
<dc:subject>to:NB racism race causal_inference causality statistics vanderweele.tyler to_teach:undergrad-ADA entableted have_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a40ba8dafa45/</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:racism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:race"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:vanderweele.tyler"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:entableted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.nber.org/papers/w19453.pdf?new_window=1">
    <title>Causal Analysis after Haavelmo</title>
    <dc:date>2013-10-13T18:54:06+00:00</dc:date>
    <link>http://www.nber.org/papers/w19453.pdf?new_window=1</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Haavelmo's seminal 1943 paper is the  ̋first rigorous treatment of causality. In it, he distinguished the definition of causal parameters from their identification. He showed that causal parameters are defined using hypothetical models that assign variation to some of the inputs determining outcomes while holding all other inputs  ̋fixed. He thus formalized and made operational Marshall's (1890) ceteris paribus analysis. We embed Haavelmo's framework into the recursive framework of Directed Acyclic Graphs (DAG) used in one influential recent approach to causality (Pearl, 2000) and in the related literature on Bayesian nets (Lauritzen, 1996). We compare an approach based on Haavelmo's methodology with a standard approach in the causal literature of DAGs– the "do-calculus" of Pearl (2009). We discuss the limitations of DAGs and in particular of the do-calculus of Pearl in securing identification of economic models. We extend our framework to consider models for simultaneous causality, a central contribution of Haavelmo (1944). In general cases, DAGs cannot be used to analyze models for simultaneous causality, but Haavelmo's approach naturally generalizes to cover it."

- From a quick scan, if there is something interesting here, it will be in the treatment of simultaneous causation.]]></description>
<dc:subject>to:NB graphical_models causality causal_inference econometrics statistics to_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b7ea45e1b373/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:graphical_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causality"/>
	<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:to_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://global.oup.com/academic/product/causation-and-its-basis-in-fundamental-physics-9780199936205">
    <title>Causation and Its Basis in Fundamental Physics - Hardcover - Douglas Kutach - Oxford University Press</title>
    <dc:date>2013-09-25T16:19:57+00:00</dc:date>
    <link>http://global.oup.com/academic/product/causation-and-its-basis-in-fundamental-physics-9780199936205</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This book is the first comprehensive attempt to solve what Hartry Field has called "the central problem in the metaphysics of causation": the problem of reconciling the need for causal notions in the special sciences with the limited role of causation in physics. If the world evolves fundamentally according to laws of physics, what place can be found for the causal regularities and principles identified by the special sciences? Douglas Kutach answers this question by invoking a novel distinction between fundamental and derivative reality and a complementary conception of reduction. He then constructs a framework that allows all causal regularities from the sciences to be rendered in terms of fundamental relations. By drawing on a methodology that focuses on explaining the results of specially crafted experiments, Kutach avoids the endless task of catering to pre-theoretical judgments about causal scenarios.
"This volume is a detailed case study that uses fundamental physics to elucidate causation, but technicalities are eschewed so that a wide range of philosophers can profit. The book is packed with innovations: new models of events, probability, counterfactual dependence, influence, and determinism. These lead to surprising implications for topics like Newcomb's paradox, action at a distance, Simpson's paradox, and more. Kutach explores the special connection between causation and time, ultimately providing a never-before-presented explanation for the direction of causation. Along the way, readers will discover that events cause themselves, that low barometer readings do cause thunderstorms after all, and that we humans routinely affect the past more than we affect the future."]]></description>
<dc:subject>to:NB books:noted causality philosophy_of_science barely-comprehensible_metaphysics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:761e943adc11/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:barely-comprehensible_metaphysics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.powells.com/biblio?isbn=0199673454">
    <title>Causation: A User's Guide by L. A. Paul - Powell's Books</title>
    <dc:date>2013-06-04T01:10:52+00:00</dc:date>
    <link>http://www.powells.com/biblio?isbn=0199673454</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Causation is at once familiar and mysterious. Many believe that the causal relation is not directly observable, but that we nevertheless can somehow detect its presence in the world. Common sense seems to have a firm grip on causation, and much work in the natural and social sciences relies on the idea. Yet neither common sense nor extensive philosophical debate has led us to anything like agreement on the correct analysis of the concept of causation, or an account of the metaphysical nature of the causal relation. Contemporary debates are driven by opposing motivations, conflicting intuitions, and unarticulated methodological assumptions.
"Causation: A User's Guide cuts a clear path through this confusing but vital landscape. L. A. Paul and Ned Hall guide the reader through the most important philosophical treatments of causation, negotiating the terrain by taking a set of examples as landmarks. Special attention is given to counterfactual and related analyses of causation. Using a methodological principle based on the close examination of potential counterexamples, they clarify the central themes of the debate about causation, and cover questions about causation involving omissions or absences, preemption and other species of redundant causation, and the possibility that causation is not transitive. Along the way, Paul and Hall examine several contemporary proposals for analyzing the nature of causation and assess their merits and overall methodological cogency.
"The book is designed to be of value both to trained specialists and those coming to the problem of causation for the first time. It provides the reader with a broad and sophisticated view of the metaphysics of the causal relation."]]></description>
<dc:subject>causality philosophy books:noted in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:c244580dc041/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.csss.washington.edu/Papers/wp128.pdf">
    <title>Single World Intervention Graphs (SWIGs): A Unification of the Counterfactual and Graphical Approaches to Causality</title>
    <dc:date>2013-05-11T03:54:30+00:00</dc:date>
    <link>http://www.csss.washington.edu/Papers/wp128.pdf</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We present a simple graphical theory unifying causal directed acyclic graphs (DAGs) and potential (aka counterfactual) outcomes via a node-splitting transformation. We introduce a new graph, the Single-World Intervention Graph (SWIG). The SWIG encodes the counterfactual independences as- sociated with a specific hypothetical intervention on the set of treatment variables. The nodes on the SWIG are the corresponding counterfactual random variables. We illustrate the theory with a number of examples. Our graphical theory of SWIGs may be used to infer the counterfactual indepen- dence relations implied by the counterfactual models developed in Robins (1986, 1987). Moreover, in the absence of hidden variables, the joint dis- tribution of the counterfactuals is identified; the identifying formula is the extended g-computation formula introduced in (Robins et al., 2004). Al- though Robins (1986, 1987) did not use DAGs we translate his algebraic results to facilitate understanding of this prior work. An attractive feature of Robins’ approach is that it largely avoids making counterfactual inde- pendence assumptions that are experimentally untestable. As an important illustration we revisit the critique of Robins’ g-computation given in (Pearl, 2009, Ch. 11.3.7); we use SWIGs to show that all of Pearl’s claims are either erroneous or based on misconceptions.
We also show that simple extensions of the formalism may be used to accommodate dynamic regimes, and to formulate non-parametric structural equation models in which assumptions relating to the absence of direct ef- fects are formulated at the population level. Finally, we show that our graphical theory also naturally arises in the context of an expanded causal Bayesian network in which we are able to observe the natural state of a variable prior to intervention."]]></description>
<dc:subject>graphical_models causal_inference causality richardson.thomas via:ogburn in_NB entableted have_read blogged robins.james_m.</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:6c9476ac3977/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:graphical_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:richardson.thomas"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:ogburn"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:entableted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:blogged"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:robins.james_m."/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.biostat.harvard.edu/robins/publications/wp100.pdf">
    <title>Alternative Graphical Causal Models and the Identification of Direct Effects</title>
    <dc:date>2013-05-10T16:57:15+00:00</dc:date>
    <link>http://www.biostat.harvard.edu/robins/publications/wp100.pdf</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We consider four classes of graphical causal models: the Finest Fully Randomized Causally Interpretable Structured Tree Graph (FFRCISTG) of Robins (1986), the agnostic causal model of Spirtes et al. (1993), the Non-Parametric Structural Equation Model (NPSEM) of Pearl (2000), and the Minimal Counterfactual Model (MCM) which we introduce. The latter is referred to as ‘minimal’ because it imposes the minimal counterfactual independence assumptions required to identify those causal contrasts representing the effect of an ideal intervention on any subset of the variables in the graph. The causal contrasts identified by an MCM are, in general, a strict subset of those identified by a NPSEM associated with the same graph. We analyze various measures of the ‘direct’ causal effect, focussing on the pure direct effect (PDE), also called the ‘natural direct effect’. We show the PDE is a parameter that may be identified in a DAG viewed as a NPSEM, but not as an MCM. In spite of this, Pearl has given a scenario in which the PDE corresponds to the intent-to-treat parameter of a randomized experiment. We resolve this apparent paradox by showing that implicit within Pearl’s account is an extended causal DAG with additional variables in which there is a causal contrast that equals the pure direct effect of Pearl’s original NPSEM. Further, this contrast is identified from observational data on the original variables. Finally we relate our results to the work of Avin et al. (2005) on path-specific causal effects."]]></description>
<dc:subject>causality graphical_models causal_inference richardson.thomas to_teach:undergrad-ADA via:ogburn in_NB robins.james_m.</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:bb3a551b7328/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:graphical_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:richardson.thomas"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:ogburn"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:robins.james_m."/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://pss.sagepub.com/content/early/2013/04/24/0956797612464058">
    <title>Political Extremism Is Supported by an Illusion of Understanding</title>
    <dc:date>2013-05-01T20:37:35+00:00</dc:date>
    <link>http://pss.sagepub.com/content/early/2013/04/24/0956797612464058</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["People often hold extreme political attitudes about complex policies. We hypothesized that people typically know less about such policies than they think they do (the illusion of explanatory depth) and that polarized attitudes are enabled by simplistic causal models. Asking people to explain policies in detail both undermined the illusion of explanatory depth and led to attitudes that were more moderate (Experiments 1 and 2). Although these effects occurred when people were asked to generate a mechanistic explanation, they did not occur when people were instead asked to enumerate reasons for their policy preferences (Experiment 2). Finally, generating mechanistic explanations reduced donations to relevant political advocacy groups (Experiment 3). The evidence suggests that people’s mistaken sense that they understand the causal processes underlying policies contributes to political polarization."]]></description>
<dc:subject>to:NB psychology political_science experimental_psychology causality via:xmarquez re:democratic_cognition</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:2ca0367049f0/</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:psychology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:political_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:experimental_psychology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:xmarquez"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:democratic_cognition"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1304.7920">
    <title>[1304.7920] From Ordinary Differential Equations to Structural Causal Models: the deterministic case</title>
    <dc:date>2013-05-01T16:31:46+00:00</dc:date>
    <link>http://arxiv.org/abs/1304.7920</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We show how, and under which conditions, the equilibrium states of a first-order Ordinary Differential Equation (ODE) system can be described with a deterministic Structural Causal Model (SCM). Our exposition sheds more light on the concept of causality as expressed within the framework of Structural Causal Models, especially for cyclic models."]]></description>
<dc:subject>to:NB causality graphical_models dynamical_systems janzing.dominik</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:4416508cda77/</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:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:graphical_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:dynamical_systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:janzing.dominik"/>
</rdf:Bag></taxo:topics>
</item>
</rdf:RDF>