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Principal Stratification and Attribution Prohibition: Good Ideas Taken Too Far

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  • Joffe Marshall

    (University of Pennsylvania)

Abstract

Pearl’s article provides a useful springboard for discussing further the benefits and drawbacks of principal stratification and the associated discomfort with attributing effects to post-treatment variables. The basic insights of the approach are important: pay close attention to modification of treatment effects by variables not observable before treatment decisions are made, and be careful in attributing effects to variables when counterfactuals are ill-defined. These insights have often been taken too far in many areas of application of the approach, including instrumental variables, censoring by death, and surrogate outcomes. A novel finding is that the usual principal stratification estimand in the setting of censoring by death is by itself of little practical value in estimating intervention effects.

Suggested Citation

  • Joffe Marshall, 2011. "Principal Stratification and Attribution Prohibition: Good Ideas Taken Too Far," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-22, September.
    • Handle: RePEc:bpj:ijbist:v:7:y:2011:i:1:n:35
    DOI: 10.2202/1557-4679.1367
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    Cited by:

    1. Dawid Philip & Didelez Vanessa, 2012. ""Imagine a Can Opener"--The Magic of Principal Stratum Analysis," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-12, July.
    2. Daniel Commenges, 2019. "Dealing with death when studying disease or physiological marker: the stochastic system approach to causality," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(3), pages 381-405, July.
    3. Mealli Fabrizia & Mattei Alessandra, 2012. "A Refreshing Account of Principal Stratification," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-19, April.
    4. Dustin M. Long & Michael G. Hudgens, 2013. "Sharpening Bounds on Principal Effects with Covariates," Biometrics, The International Biometric Society, vol. 69(4), pages 812-819, December.
    5. Judea Pearl, 2012. "Bias and Causation, Models and Judgment for Valid Comparisons by WEISBERG, H. I," Biometrics, The International Biometric Society, vol. 68(2), pages 659-660, June.
    6. Shinohara Russell T. & Frangakis Constantine E. & Platz Elizabeth & Tsilidis Konstantinos, 2012. "Designs Combining Instrumental Variables with Case-Control: Estimating Principal Strata Causal Effects," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-21, January.
    7. Greene Tom & Joffe Marshall & Hu Bo & Li Liang & Boucher Ken, 2013. "The Balanced Survivor Average Causal Effect," The International Journal of Biostatistics, De Gruyter, vol. 9(2), pages 291-306, May.

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