Causal Inference Using Potential Outcomes: Design, Modeling, Decisions

• Activity Number: 390
• Type: Invited
• Date/Time: Wednesday, August 11, 2004 : 4:00 PM to 5:50 PM
• Sponsor: Cmte of Presidents of Statistics Societies (COPSS)
• Author(s): Donald B. Rubin*+
• Companies: Harvard University

Abstract

Causal effects are defined as the comparison of potential outcomes under different treatments on a common set of units. Observed values of the potential outcomes are revealed by the assignment mechanism--a probabilistic model for the treatment each unit receives as a function of covariates and potential outcomes. Fisher made tremendous contributions to causal inference through his work on the design of randomized field experiments, but the potential outcomes perspective applies to other complex experiments and nonrandomized studies as well. As noted by Kempthorne in his discussion of Savage's Fisher lecture, Fisher never bridged his work on experimental design and his work on parametric modeling, a bridge that appears nearly automatic with an appropriate view of the potential outcomes framework, where the potential outcomes and covariates are given a Bayesian distribution to complete the model specification. Also, this framework crisply separates scientific inference for causal effects and decisions based on such inference, a distinction evident in Fisher's discussion of tests of significance versus tests in an accept/reject context.