On the role of counterfactuals in inferring causal effects

Foundations of Science 9 (1):65-101 (2004)
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Abstract

Causal inference in the empiricalsciences is based on counterfactuals. The mostcommon approach utilizes a statistical model ofpotential outcomes to estimate causal effectsof treatments. On the other hand, one leadingapproach to the study of causation inphilosophical logic has been the analysis ofcausation in terms of counterfactualconditionals. This paper discusses and connectsboth approaches to counterfactual causationfrom philosophy and statistics. Specifically, Ipresent the counterfactual account of causationin terms of Lewis's possible-world semantics,and reformulate the statistical potentialoutcome framework using counterfactualconditionals. This procedure highlights variousproperties and mechanisms of the statisticalmodel.

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Citations of this work

A glimpse of the.Stathis Psillos - 2004 - Perspectives on Science 12 (3):288-319.
Selection and explanation.Alexander Bird - 2007 - In Johannes Persson & Petri Ylikoski (eds.), Rethinking Explanation. Springer. pp. 131--136.

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References found in this work

Counterfactuals.David K. Lewis - 1973 - Malden, Mass.: Blackwell.
Causality: Models, Reasoning and Inference.Judea Pearl - 2000 - New York: Cambridge University Press.
The direction of time.Hans Reichenbach - 1956 - Mineola, N.Y.: Dover Publications. Edited by Maria Reichenbach.
Counterfactuals.David Lewis - 1973 - Tijdschrift Voor Filosofie 36 (3):602-605.
Causality and explanation.Wesley C. Salmon - 1998 - New York: Oxford University Press.

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