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A Refreshing Account of Principal Stratification

Author

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  • Mealli Fabrizia

    (University of Florence)

  • Mattei Alessandra

    (University of Florence)

Abstract

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.

Suggested Citation

  • 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.
    • Handle: RePEc:bpj:ijbist:v:8:y:2012:i:1:n:8
    DOI: 10.1515/1557-4679.1380
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    References listed on IDEAS

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    1. Paolo Frumento & Fabrizia Mealli & Barbara Pacini & Donald B. Rubin, 2012. "Evaluating the Effect of Training on Wages in the Presence of Noncompliance, Nonemployment, and Missing Outcome Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(498), pages 450-466, June.
    2. Egleston Brian L, 2011. "Response to Pearl's Comments on Principal Stratification," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-3, May.
    3. Gilbert Peter B. & Hudgens Michael G. & Wolfson Julian, 2011. "Commentary on "Principal Stratification -- a Goal or a Tool?" by Judea Pearl," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-15, September.
    4. Zhihong Cai & Manabu Kuroki & Judea Pearl & Jin Tian, 2008. "Bounds on Direct Effects in the Presence of Confounded Intermediate Variables," Biometrics, The International Biometric Society, vol. 64(3), pages 695-701, September.
    5. 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.
    6. Sjolander Arvid, 2011. "Reaction to Pearl's Critique of Principal Stratification," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-5, April.
    7. Zhang, Junni L. & Rubin, Donald B. & Mealli, Fabrizia, 2009. "Likelihood-Based Analysis of Causal Effects of Job-Training Programs Using Principal Stratification," Journal of the American Statistical Association, American Statistical Association, vol. 104(485), pages 166-176.
    8. A. Mattei & F. Mealli, 2007. "Application of the Principal Stratification Approach to the Faenza Randomized Experiment on Breast Self-Examination," Biometrics, The International Biometric Society, vol. 63(2), pages 437-446, June.
    9. Guido W. Imbens, 2010. "Better LATE Than Nothing: Some Comments on Deaton (2009) and Heckman and Urzua (2009)," Journal of Economic Literature, American Economic Association, vol. 48(2), pages 399-423, June.
    10. Baker Stuart G & Lindeman Karen S & Kramer Barnett S, 2011. "Clarifying the Role of Principal Stratification in the Paired Availability Design," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-11, May.
    11. Imai, Kosuke, 2008. "Sharp bounds on the causal effects in randomized experiments with "truncation-by-death"," Statistics & Probability Letters, Elsevier, vol. 78(2), pages 144-149, February.
    12. Hua Chen & Zhi Geng & Jinzhu Jia, 2007. "Criteria for surrogate end points," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(5), pages 919-932, November.
    13. VanderWeele Tyler J, 2011. "Principal Stratification -- Uses and Limitations," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-14, July.
    14. VanderWeele, Tyler J., 2008. "Simple relations between principal stratification and direct and indirect effects," Statistics & Probability Letters, Elsevier, vol. 78(17), pages 2957-2962, December.
    15. Prentice Ross, 2011. "Invited Commentary on Pearl and Principal Stratification," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-15, August.
    16. Edward Vytlacil & James J. Heckman, 2001. "Policy-Relevant Treatment Effects," American Economic Review, American Economic Association, vol. 91(2), pages 107-111, May.
    17. Imbens, Guido W & Angrist, Joshua D, 1994. "Identification and Estimation of Local Average Treatment Effects," Econometrica, Econometric Society, vol. 62(2), pages 467-475, March.
    18. Pearl Judea, 2011. "Principal Stratification -- a Goal or a Tool?," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-13, March.
    19. Mealli, Fabrizia & Rubin, Donald B., 2003. "Assumptions allowing the estimation of direct causal effects," Journal of Econometrics, Elsevier, vol. 112(1), pages 79-87, January.
    20. Jin, Hui & Rubin, Donald B., 2008. "Principal Stratification for Causal Inference With Extended Partial Compliance," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 101-111, March.
    21. David S. Lee, 2009. "Training, Wages, and Sample Selection: Estimating Sharp Bounds on Treatment Effects," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 76(3), pages 1071-1102.
    22. Donald B. Rubin, 2005. "Causal Inference Using Potential Outcomes: Design, Modeling, Decisions," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 322-331, March.
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    Cited by:

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    2. Zhichao Jiang & Peng Ding & Zhi Geng, 2016. "Principal causal effect identification and surrogate end point evaluation by multiple trials," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(4), pages 829-848, September.
    3. Michela Baccini & Alessandra Mattei & Fabrizia Mealli, 2015. "Bayesian inference for causal mechanisms with application to a randomized study for postoperative pain control," Econometrics Working Papers Archive 2015_06, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
    4. Laura Forastiere & Fabrizia Mealli & Tyler J. VanderWeele, 2016. "Identification and Estimation of Causal Mechanisms in Clustered Encouragement Designs: Disentangling Bed Nets Using Bayesian Principal Stratification," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 510-525, April.
    5. Stephens Alisa & Keele Luke & Joffe Marshall, 2016. "Generalized Structural Mean Models for Evaluating Depression as a Post-treatment Effect Modifier of a Jobs Training Intervention," Journal of Causal Inference, De Gruyter, vol. 4(2), pages 1-17, September.
    6. Tianmeng Lyu & Björn Bornkamp & Guenther Mueller‐Velten & Heinz Schmidli, 2023. "Bayesian inference for a principal stratum estimand on recurrent events truncated by death," Biometrics, The International Biometric Society, vol. 79(4), pages 3792-3802, December.
    7. Fabrizia Mealli & Barbara Pacini & Elena Stanghellini, 2016. "Identification of Principal Causal Effects Using Additional Outcomes in Concentration Graphs," Journal of Educational and Behavioral Statistics, , vol. 41(5), pages 463-480, October.
    8. Chanmin Kim & Lucas R. F. Henneman & Christine Choirat & Corwin M. Zigler, 2020. "Health effects of power plant emissions through ambient air quality," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(4), pages 1677-1703, October.
    9. Stephens Alisa & Joffe Marshall & Keele Luke, 2016. "Generalized Structural Mean Models for Evaluating Depression as a Post-treatment Effect Modifier of a Jobs Training Intervention," Journal of Causal Inference, De Gruyter, vol. 4(2), pages 1, September.
    10. Alessandra Mattei & Fabrizia Mealli & Barbara Pacini, 2014. "Identification of causal effects in the presence of nonignorable missing outcome values," Biometrics, The International Biometric Society, vol. 70(2), pages 278-288, June.
    11. Bijwaard, G.E.; & Jones, A.M.;, 2019. "Education and life-expectancy and how the relationship is mediated through changes in behaviour: a principal stratification approach for hazard rates," Health, Econometrics and Data Group (HEDG) Working Papers 19/05, HEDG, c/o Department of Economics, University of York.

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