Causal mediation analysis
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.
Volume (Year): 11 (2011)
Issue (Month): 4 (December)
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References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Maarten L. Buis, 2010.
"Direct and indirect effects in a logit model,"
StataCorp LP, vol. 10(1), pages 11-29, March.
- Maarten L. Buis, 2008. "Direct and indirect effects in a logit model," German Stata Users' Group Meetings 2008 02, Stata Users Group.
- Ulrich Kohler & Kristian Bernt Karlson & Anders Holm, 2011. "Comparing coefficients of nested nonlinear probability models," Stata Journal, StataCorp LP, vol. 11(3), pages 420-438, September. Full references (including those not matched with items on IDEAS)