Statistical Science

Causal Inference Through Potential Outcomes and Principal Stratification: Application to Studies with “Censoring” Due to Death

Donald B. Rubin

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Causal inference is best understood using potential outcomes. This use is particularly important in more complex settings, that is, observational studies or randomized experiments with complications such as noncompliance. The topic of this lecture, the issue of estimating the causal effect of a treatment on a primary outcome that is “censored” by death, is another such complication. For example, suppose that we wish to estimate the effect of a new drug on Quality of Life (QOL) in a randomized experiment, where some of the patients die before the time designated for their QOL to be assessed. Another example with the same structure occurs with the evaluation of an educational program designed to increase final test scores, which are not defined for those who drop out of school before taking the test. A further application is to studies of the effect of job-training programs on wages, where wages are only defined for those who are employed. The analysis of examples like these is greatly clarified using potential outcomes to define causal effects, followed by principal stratification on the intermediated outcomes (e.g., survival).

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Statist. Sci. Volume 21, Number 3 (2006), 299-309.

First available in Project Euclid: 20 December 2006

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Missing data quality of life Rubin causal model truncation due to death


Rubin, Donald B. Causal Inference Through Potential Outcomes and Principal Stratification: Application to Studies with “Censoring” Due to Death. Statist. Sci. 21 (2006), no. 3, 299--309. doi:10.1214/088342306000000114.

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