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Abstract Details

Activity Number: 311
Type: Invited
Date/Time: Tuesday, July 31, 2012 : 10:30 AM to 12:20 PM
Sponsor: Social Statistics Section
Abstract - #303752
Title: Causal Inference from the Missing Data Perspective
Author(s): Donald B Rubin*+
Companies: Harvard University
Address: 1 Oxford Street, Cambridge, MA, 02138, USA
Keywords: potential outcomes ; principal stratification ; Rubin Causal Model

From the mid-1970s (Rubin, 1974, 1976, 1978), I have felt that viewing all causal inference as the problem of missing potential outcomes has been the most effective method for conceptualizing and clarifying critical issues. I feel even more strongly about this today because of the success of this idea in bridging different scholarly cultures, for example in statistics, economics, epidemiology, and related fields. The recent explosion of applications of principal stratification based on the missing data perspective lends even more support to the claim that this is an exceedingly insightful perspective. Moreover, the perspective makes available a host of clever computational algorithms, including the EM family and the MCMC family.

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