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13
Transportability across studies: A formal approach
, 2010
"... We provide a formal definition of the notion of “transportability, ” or “external validity, ” which we view as a license to transfer causal information learned in experimental studies to a different environment, in which only observational studies can be conducted. We introduce a formal representati ..."
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We provide a formal definition of the notion of “transportability, ” or “external validity, ” which we view as a license to transfer causal information learned in experimental studies to a different environment, in which only observational studies can be conducted. We introduce a formal representation called “selection diagrams ” for expressing knowledge about differences and commonalities between populations of interest and, using this representation, we derive procedures for deciding whether causal effects in the target environment can be inferred from experimental findings in a different environment. When the answer is affirmative, the procedures identify the set of experimental and observational studies that need be conducted to license the transport. We further demonstrate how transportability analysis can guide the transfer of knowledge among nonexperimental studies to minimize remeasurement cost and improve prediction power. We further provide a causally principled definition of “surrogate endpoint ” and show that the theory of transportability can assist the identification of valid surrogates in a complex network of causeeffect relationships. 1 Introduction: Threats
Principal stratification a goal or a tool? The
 International Journal of Biostatistics 7. Article
"... Principal stratification has recently become a popular tool to address certain causal inference questions particularly in dealing with postrandomization factors in randomized trials. Here we analyze the conceptual basis for this framework and invite response to clarify the value of principal strati ..."
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Cited by 7 (5 self)
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Principal stratification has recently become a popular tool to address certain causal inference questions particularly in dealing with postrandomization factors in randomized trials. Here we analyze the conceptual basis for this framework and invite response to clarify the value of principal stratification in estimating causal effects of interest.
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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Cited by 7 (4 self)
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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.
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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Cited by 6 (2 self)
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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
Applications of causally defined direct and indirect effects in mediation analysis using SEM
 University of California
"... Judea Pearl for helpful advice This paper summarizes some of the literature on causal effects in mediation analysis. It presents causallydefined direct and indirect effects for continuous, binary, ordinal, nominal, and count variables. The expansion to noncontinuous mediators and outcomes offers a ..."
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Cited by 3 (0 self)
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Judea Pearl for helpful advice This paper summarizes some of the literature on causal effects in mediation analysis. It presents causallydefined direct and indirect effects for continuous, binary, ordinal, nominal, and count variables. The expansion to noncontinuous mediators and outcomes offers a broader array of causal mediation analyses than previously considered in structural equation modeling practice. A new result is the ability to handle mediation by a nominal variable. Examples with a binary outcome and a binary, ordinal or nominal mediator are given using Mplus to compute the effects. The causal effects require strong assumptions even in randomized designs, especially sequential ignorability, which is presumably often violated to some extent due to mediatoroutcome confounding. To study the effects of violating this assumption, it is shown how a sensitivity analysis can be carried out. This can be used both in planning a new study and in evaluating the results of an existing study.
of LaborEconometric Mediation Analyses: Identifying the Sources of Treatment Effects from Experimentally Estimated Production Technologies with Unmeasured and
, 2013
"... Any opinions expressed here are those of the author(s) and not those of IZA. Research published in this series may include views on policy, but the institute itself takes no institutional policy positions. The IZA research network is committed to the IZA Guiding Principles of Research Integrity. The ..."
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Cited by 3 (1 self)
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Any opinions expressed here are those of the author(s) and not those of IZA. Research published in this series may include views on policy, but the institute itself takes no institutional policy positions. The IZA research network is committed to the IZA Guiding Principles of Research Integrity. The Institute for the Study of Labor (IZA) in Bonn is a local and virtual international research center and a place of communication between science, politics and business. IZA is an independent nonprofit organization supported by Deutsche Post Foundation. The center is associated with the University of Bonn and offers a stimulating research environment through its international network, workshops and conferences, data service, project support, research visits and doctoral program. IZA engages in (i) original and internationally competitive research in all fields of labor economics, (ii) development of policy concepts, and (iii) dissemination of research results and concepts to the interested public. IZA Discussion Papers often represent preliminary work and are circulated to encourage discussion. Citation of such a paper should account for its provisional character. A revised version may be
Statistics and Causality: Separated to Reunite Commentary on Bryan Dowd’s “Separated at Birth”
, 2010
"... Bryan Dowd (2010) should be commended for laying before us the historical roots of the tensions between statisticians and econometricians which, until today, perpetuate the myth that causal inference is somehow confusing, enigmatic or controversial. While modern analysis has proven this myth baseles ..."
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Bryan Dowd (2010) should be commended for laying before us the historical roots of the tensions between statisticians and econometricians which, until today, perpetuate the myth that causal inference is somehow confusing, enigmatic or controversial. While modern analysis has proven this myth baseless, it is often the historical accounts that put things in the
The Causal Mediation Formula – A practitioner guide
, 2011
"... Recent advances in causal inference have given rise to a general and easytouse estimator for assessing the extent to which the effect of one variable on another is mediated by a third. This estimator, called Mediation Formula, is applicable to nonlinear models with both discrete and continuous var ..."
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Recent advances in causal inference have given rise to a general and easytouse estimator for assessing the extent to which the effect of one variable on another is mediated by a third. This estimator, called Mediation Formula, is applicable to nonlinear models with both discrete and continuous variables, and permits the evaluation of pathspecific effects with minimal assumptions regarding the datagenerating process. We demonstrate the use of the Mediation Formula in simple examples and illustrate why parametric methods of analysis yield distorted results, even when parameters are known precisely. We stress the importance of distinguishing between the necessary and sufficient interpretations of “mediatedeffect ” and show how to estimate the two components in nonlinear systems with continuous and categorical variables.
The algorithmization of counterfactuals
, 2011
"... Recent advances in causal reasoning have given rise to a computation model that emulates the process by which humans generate, evaluate and distinguish counterfactual sentences. Though compatible with the “possible world ” account, this model enjoys the advantages of representational economy, algori ..."
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Recent advances in causal reasoning have given rise to a computation model that emulates the process by which humans generate, evaluate and distinguish counterfactual sentences. Though compatible with the “possible world ” account, this model enjoys the advantages of representational economy, algorithmic simplicity and conceptual clarity. Using this model, the paper demonstrates the processing of counterfactual sentences on a classical example due to Ernst Adam. It then gives a panoramic view of several applications where counterfactual reasoning has benefited problem areas in the empirical sciences. 1