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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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    3. 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.
    4. 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".
    5. 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.
    6. 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.
    7. Guido Imbens, 2019. "Potential Outcome and Directed Acyclic Graph Approaches to Causality: Relevance for Empirical Practice in Economics," NBER Working Papers 26104, National Bureau of Economic Research, Inc.
    8. 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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