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The Mediation Formula: A guide to the assessment of causal pathways in nonlinear models
 STATISTICAL CAUSALITY. FORTHCOMING.
, 2011
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Transportability of causal and statistical relations: A formal approach
 In Proceedings of the TwentyFifth National Conference on Artificial Intelligence. AAAI Press, Menlo Park, CA
, 2011
"... We address the problem of transferring information learned from experiments to a different environment, in which only passive observations can be collected. We introduce a formal representation called “selection diagrams ” for expressing knowledge about differences and commonalities between environm ..."
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Cited by 14 (9 self)
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We address the problem of transferring information learned from experiments to a different environment, in which only passive observations can be collected. We introduce a formal representation called “selection diagrams ” for expressing knowledge about differences and commonalities between environments and, using this representation, we derive procedures for deciding whether effects in the target environment can be inferred from experiments conducted elsewhere. When the answer is affirmative, the procedures identify the set of experiments and observations that need be conducted to license the transport. We further discuss how transportability analysis can guide the transfer of knowledge in nonexperimental learning to minimize remeasurement cost and improve prediction power.
Measurement bias and effect restoration in causal inference
, 2010
"... This paper highlights several areas where graphical techniques can be harnessed to address the problem of measurement errors in causal inference. In particular, the paper discusses the control of partially observable confounders in parametric and non parametric models and the computational problem o ..."
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Cited by 3 (1 self)
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This paper highlights several areas where graphical techniques can be harnessed to address the problem of measurement errors in causal inference. In particular, the paper discusses the control of partially observable confounders in parametric and non parametric models and the computational problem of obtaining biasfree effect estimates in such models.
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 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
External Validity and Transportability: A Formal Approach
, 2011
"... We provide a formal definition of the notion of “transportability, ” or “external validity, ” as a license to transfer causal information from experimental studies to a different population in which only observational studies can be conducted. We introduce a formal representation called “selection d ..."
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We provide a formal definition of the notion of “transportability, ” or “external validity, ” as a license to transfer causal information from experimental studies to a different population in which only observational studies can be conducted. We introduce a formal representation called “selection diagrams ” for expressing differences and commonalities between populations of interest and, using this representation, we derive procedures for deciding whether causal effects in the target population can be inferred from experimental findings in a different population. 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 discuss how transportability analysis can guide the transfer of knowledge in nonexperimental learning to minimize remeasurement cost and improve prediction power.
RESEARCH ARTICLE Open Access
"... Combining directed acyclic graphs and the changeinestimate procedure as a novel approach to adjustmentvariable selection in epidemiology David Evans 1,2,3,4 * , Basile Chaix 1,3, Thierry Lobbedez 4,5, Christian Verger 4 and Antoine Flahault 1,2 Background: Directed acyclic graphs (DAGs) are an ef ..."
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Combining directed acyclic graphs and the changeinestimate procedure as a novel approach to adjustmentvariable selection in epidemiology David Evans 1,2,3,4 * , Basile Chaix 1,3, Thierry Lobbedez 4,5, Christian Verger 4 and Antoine Flahault 1,2 Background: Directed acyclic graphs (DAGs) are an effective means of presenting expertknowledge assumptions when selecting adjustment variables in epidemiology, whereas the changeinestimate procedure is a common statisticsbased approach. As DAGs imply specific empirical relationships which can be explored by the changeinestimate procedure, it should be possible to combine the two approaches. This paper proposes such an approach which aims to produce welladjusted estimates for a given research question, based on plausible DAGs consistent with the data at hand, combining prior knowledge and standard regression methods. Methods: Based on the relationships laid out in a DAG, researchers can predict how a collapsible estimator (e.g. risk ratio or risk difference) for an effect of interest should change when adjusted on different variable sets. Implied and observed patterns can then be compared to detect inconsistencies and so guide adjustmentvariable selection.