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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 Twenty-Fifth 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 ..."
Abstract
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Cited by 7 (5 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 non-experimental learning to minimize re-measurement 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 2 (0 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 bias-free 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
