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Principal ignorability in mediation analysis: through and beyond sequential ignorability

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  • Laura Forastiere
  • Alessandra Mattei
  • Peng Ding

Abstract

SummaryIn causal mediation analysis, the definitions of the natural direct and indirect effects involve potential outcomes that can never be observed, so-called a priori counterfactuals. This conceptual challenge translates into issues in identification, which requires strong and often unverifiable assumptions, including sequential ignorability. Alternatively, we can deal with post-treatment variables using the principal stratification framework, where causal effects are defined as comparisons of observable potential outcomes. We establish a novel bridge between mediation analysis and principal stratification, which helps to clarify and weaken the commonly used identifying assumptions for natural direct and indirect effects. Using principal stratification, we show how sequential ignorability extrapolates from observable potential outcomes to a priori counterfactuals, and propose alternative weaker principal ignorability-type assumptions. We illustrate the key concepts using a clinical trial.

Suggested Citation

  • Laura Forastiere & Alessandra Mattei & Peng Ding, 2018. "Principal ignorability in mediation analysis: through and beyond sequential ignorability," Biometrika, Biometrika Trust, vol. 105(4), pages 979-986.
    • Handle: RePEc:oup:biomet:v:105:y:2018:i:4:p:979-986.
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    File URL: http://hdl.handle.net/10.1093/biomet/asy053
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    Cited by:

    1. Kristin Brandl & Elizabeth Moore & Camille Meyer & Jonathan Doh, 2022. "The impact of multinational enterprises on community informal institutions and rural poverty," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 53(6), pages 1133-1152, August.
    2. Zhichao Jiang & Shu Yang & Peng Ding, 2022. "Multiply robust estimation of causal effects under principal ignorability," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(4), pages 1423-1445, September.
    3. Shuxi Zeng & Elizabeth C. Lange & Elizabeth A. Archie & Fernando A. Campos & Susan C. Alberts & Fan Li, 2023. "A Causal Mediation Model for Longitudinal Mediators and Survival Outcomes with an Application to Animal Behavior," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 28(2), pages 197-218, June.

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