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On the Role of Counterfactuals in Inferring Causal Effects

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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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  1. Department of Economics – Center for Labor Economics, University of California at Berkeley, 549 Evans Hall #3880, Berkeley, CA, 94720

    Jochen Kluve

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Kluve, J. On the Role of Counterfactuals in Inferring Causal Effects. Foundations of Science 9, 65–101 (2004). https://doi.org/10.1023/B:FODA.0000014881.82061.7b

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