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Causal mediation analysis

Author

Listed:
  • Raymond Hicks

    (Princeton University)

  • Dustin Tingley

    (Harvard University)

Abstract

Estimating the mechanisms that connect explanatory variables with the explained variable, also known as "mediation analysis," is central to a variety of social-science fields, especially psychology, and increasingly to fields like epidemiology. Recent work on the statistical methodology behind mediation analysis points to limitations in earlier methods. We implement in Stata computational approaches based on recent developments in the statistical methodology of mediation analysis. In particular, we provide functions for the correct calculation of causal mediation effects using several different types of parametric models, as well as the calculation of sensitivity analyses for violations to the key identifying assumption required for interpreting mediation results causally.

Suggested Citation

  • Raymond Hicks & Dustin Tingley, 2011. "Causal mediation analysis," Stata Journal, StataCorp LLC, vol. 11(4), pages 605-619, December.
    • Handle: RePEc:tsj:stataj:v:11:y:2011:i:4:p:605-619
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    References listed on IDEAS

    as
    1. Maarten L. Buis, 2010. "Direct and indirect effects in a logit model," Stata Journal, StataCorp LLC, vol. 10(1), pages 11-29, March.
    2. Ulrich Kohler & Kristian Bernt Karlson & Anders Holm, 2011. "Comparing coefficients of nested nonlinear probability models," Stata Journal, StataCorp LLC, vol. 11(3), pages 420-438, September.
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