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- An Introduction to Causal Inference by Pearl, Judea
- Principal Stratification: A Broader Vision by Shrier, Ian/ Kaufman, Jay S./ Platt, Robert W. and Steele, Russell J.
- A Weighting Analogue to Pair Matching in Propensity Score Analysis by Li, Liang and Greene, Tom
- Exact Nonparametric Confidence Bands for the Survivor Function by Matthews, David
- Accuracy of Conventional and Marginal Structural Cox Model Estimators: A Simulation Study by Xiao, Yongling/ Abrahamowicz, Michal and Moodie, Erica E. M.
A Refreshing Account of Principal Stratification
1University of Florence
2University of Florence
Citation Information: The International Journal of Biostatistics. Volume 8, Issue 1, ISSN (Online) 1557-4679, DOI: 10.1515/1557-4679.1380, April 2012
- Published Online:
Pearl (2011) invites researchers to contribute to a discussion on the logic and utility of principal stratification in causal inference, raising some thought-provoking questions. In our commentary, we discuss the role of principal stratification in causal inference, describing why we view the principal stratification framework as useful for addressing causal inference problems where causal estimands are defined in terms of intermediate outcomes. We focus on mediation analysis and principal stratification analysis, showing that they generally involve different causal estimands and answer different questions. We argue that even when principal stratification may not answer the causal questions of primary interest, it can be a preliminary analysis of the data to assess the plausibility of identifying assumptions. We also discuss the use of principal stratification to address issues of surrogate outcomes. Our discussion stresses that a principal stratification analysis should account for all the principal strata and evaluate the distributions of potential outcomes in each of the principal strata. To this end, we view a Bayesian analysis particularly suited for drawing inference on principal strata membership and principal strata effects.