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Alternative graphical causal models and the identification of direct effects
, 2009
"... We consider four classes of graphical causal models: the Finest Fully Randomized Causally ..."
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We consider four classes of graphical causal models: the Finest Fully Randomized Causally
The docalculus revisited
, 2012
"... The docalculus was developed in 1995 to facilitate the identification of causal effects in nonparametric models. The completeness proofs of [Huang and Valtorta, 2006] and [Shpitser and Pearl, 2006] and the graphical criteria of [Tian and Shpitser, 2010] have laid this identification problem to res ..."
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The docalculus was developed in 1995 to facilitate the identification of causal effects in nonparametric models. The completeness proofs of [Huang and Valtorta, 2006] and [Shpitser and Pearl, 2006] and the graphical criteria of [Tian and Shpitser, 2010] have laid this identification problem to rest. Recent explorations unveil the usefulness of the docalculus in three additional areas: mediation analysis [Pearl, 2012], transportability [Pearl and Bareinboim, 2011] and metasynthesis. Metasynthesis (freshly coined) is the task of fusing empirical results from several diverse studies, conducted on heterogeneous populations and under different conditions, so as to synthesize an estimate of a causal relation in some target environment, potentially different from those under study. The talk surveys these results with emphasis on the challenges posed by metasynthesis. For background material, see
Statistics and Causal Inference: A Review
, 2003
"... This paper aims at assisting empirical researchers benefit from recent advances in causal inference. The paper stresses the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assump ..."
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This paper aims at assisting empirical researchers benefit from recent advances in causal inference. The paper stresses the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions that underly all causal inferences, the languages used in formulating those assumptions, and the conditional nature of causal claims inferred from nonexperimental studies. These emphases are illustrated through a brief survey of recent results, including the control of confounding, the assessment of causal effects, the interpretation of counterfactuals, and a symbiosis between counterfactual and graphical methods of analysis.
2007): “Defining and estimating intervention effects for groups that will develop an auxiliary outcome
 Statistical Science
"... Abstract. It has recently become popular to define treatment effects for subsets of the target population characterized by variables not observable at the time a treatment decision is made. Characterizing and estimating such treatment effects is tricky; the most popular but naive approach inappropri ..."
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Abstract. It has recently become popular to define treatment effects for subsets of the target population characterized by variables not observable at the time a treatment decision is made. Characterizing and estimating such treatment effects is tricky; the most popular but naive approach inappropriately adjusts for variables affected by treatment and so is biased. We consider several appropriate ways to formalize the effects: principal stratification, stratification on a single potential auxiliary variable, stratification on an observed auxiliary variable and stratification on expected levels of auxiliary variables. We then outline identifying assumptions for each type of estimand. We evaluate the utility of these estimands and estimation procedures for decision making and understanding causal processes, contrasting them with the concepts of direct and indirect effects. We motivate our development with examples from nephrology and cancer screening, and use simulated data and real data on cancer screening to illustrate the estimation methods. Key words and phrases: Causality, direct effects, interaction, effect modification, bias, principal stratification.
A comparison of collaborative and topdown approach to the use of science in policy: Establishing marine protected areas
 in California.” Policy Studies Journal
, 2004
"... decision making about risk. These two approaches are widely applicable to environmental decisionmaking and are exemplified by two attempts to establish Marine Protected Areas (MPAs) in California with the implementation of the 1999 Marine Life Protection Act. The first attempt, which parallels the ..."
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decision making about risk. These two approaches are widely applicable to environmental decisionmaking and are exemplified by two attempts to establish Marine Protected Areas (MPAs) in California with the implementation of the 1999 Marine Life Protection Act. The first attempt, which parallels the NRC’s 1983 linear scientific approach, was a topdown process that involved a Master Plan Team of scientists who created a proposal before gathering public input. The second attempt, which parallels the NRC’s 1996 analytic and deliberative approach, involved a diverse set of stakeholders, including scientists, who worked in a collaborative process to provide a range of recommendations. We apply a threetiered model of elite belief systems drawn from the Advocacy Coalition Framework to show that stakeholder preferences for either of these approaches is a function of their deep core beliefs. Stakeholders with strong preferences for scientific management support empirical claims for the benefits of MPAs and are more optimistic about the linear scientific approach compared to the analytic and deliberative approach for protecting major habitats, avoiding adverse fishing effects, and avoiding unfair agency domination. In contrast, stakeholders with procollaborative beliefs respect local knowledge and are more optimistic about the analytic and deliberative approach compared to the linear scientific approach for avoiding adverse fishing effects and unfair agency domination. Several studies have analyzed the use of collaborative institutions for resolving
Transportability of Causal Effects: Completeness Results
, 2012
"... The study of transportability aims to identify conditions under which causal information learned from experiments can be reused in a different environment where only passive observations can be collected. The theory introduced in [Pearl and Bareinboim, 2011] (henceforth [PB, 2011]) defines formal co ..."
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The study of transportability aims to identify conditions under which causal information learned from experiments can be reused in a different environment where only passive observations can be collected. The theory introduced in [Pearl and Bareinboim, 2011] (henceforth [PB, 2011]) defines formal conditions for such transfer but falls short of providing an effective procedure for deciding whether transportability is feasible for a given set of assumptions about differences between the source and target domains. This paper provides such procedure. It establishes a necessary and sufficient condition for deciding when causal effects in the target domain are estimable from both the statistical information available and the causal information transferred from the experiments. The paper further provides a complete algorithm for computing the transport formula, that is, a way of fusing experimental and observational information to synthesize an estimate of the desired causal relation.
Trygve Haavelmo and the Emergence of Causal Calculus
, 2012
"... Haavelmo was the first to recognize the capacity of economic models to guide policies. This paper describes some of the barriers that Haavelmo’s ideas have had (and still have) to overcome, and lays out a logical framework for capturing the relationships between theory, data and policy questions. Th ..."
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Haavelmo was the first to recognize the capacity of economic models to guide policies. This paper describes some of the barriers that Haavelmo’s ideas have had (and still have) to overcome, and lays out a logical framework for capturing the relationships between theory, data and policy questions. The mathematical tools that emerge from this framework now enable investigators to answer complex policy and counterfactual questions using embarrassingly simple routines, some by mere inspection of the model’s structure. Several such problems are illustrated by examples, including misspecification tests, identification, mediation and introspection. Finally, we observe that modern economists are largely unaware of the benefits that Haavelmo’s ideas bestow upon them and, as a result, econometric research has not fully utilized modern advances in causal analysis. 1
The Foundations of Causal Inference
 SUBMITTED TO SOCIOLOGICAL METHODOLOGY.
, 2010
"... This paper reviews recent advances in the foundations of causal inference and introduces a systematic methodology for defining, estimating and testing causal claims in experimental and observational studies. It is based on nonparametric structural equation models (SEM) – a natural generalization of ..."
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This paper reviews recent advances in the foundations of causal inference and introduces a systematic methodology for defining, estimating and testing causal claims in experimental and observational studies. It is based on nonparametric structural equation models (SEM) – a natural generalization of those used by econometricians and social scientists in the 195060s, and provides a coherent mathematical foundation for the analysis of causes and counterfactuals. In particular, the paper surveys the development of mathematical tools for inferring the effects of potential interventions (also called “causal effects” or “policy evaluation”), as well as direct and indirect effects (also known as “mediation”), in both linear and nonlinear systems. Finally, the paper clarifies the role of propensity score matching in causal analysis, defines the relationships between the structural and
Complete Identification Methods for the Causal Hierarchy
"... We consider a hierarchy of queries about causal relationships in graphical models, where each level in the hierarchy requires more detailed information than the one below. The hierarchy consists of three levels: associative relationships, derived from a joint distribution over the observable variabl ..."
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We consider a hierarchy of queries about causal relationships in graphical models, where each level in the hierarchy requires more detailed information than the one below. The hierarchy consists of three levels: associative relationships, derived from a joint distribution over the observable variables; causeeffect relationships, derived from distributions resulting from external interventions; and counterfactuals, derived from distributions that span multiple “parallel worlds ” and resulting from simultaneous, possibly conflicting observations and interventions. We completely characterize cases where a given causal query can be computed from information lower in the hierarchy, and provide algorithms that accomplish this computation. Specifically, we show when effects of interventions can be computed from observational studies, and when probabilities of counterfactuals can be computed from experimental studies. We also provide a graphical characterization of those queries which cannot be computed (by any method) from queries at a lower layer of the hierarchy.