Nonparametric estimation of natural direct and indirect effects based on inverse probability weighting
Martin Huber and
Tsung Chih Lai
No 482, FSES Working Papers from Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland
Using a sequential conditional independence assumption, this paper discusses fully nonparametric estimation of natural direct and indirect causal effects in causal mediation analysis based on inverse probability weighting. We propose estimators of the average indirect effect of a binary treatment, which operates through intermediate variables (or mediators) on the causal path between the treatment and the outcome, as well as the unmediated direct effect. In a first step, treatment propensity scores given the mediator and observed covariates or given covariates alone are estimated by nonparametric series logit estimation. In a second step, they are used to reweigh observations in order to estimate the effects of interest. We establish root-n consistency and asymptotic normality of this approach as well as a weighted version thereof. The latter allows evaluating effects on specific subgroups like the treated, for which we derive the asymptotic properties under estimated propensity scores. We also provide a simulation study and an application to an information intervention about male circumcisions.
Keywords: causal mechanisms; direct effects; indirect effects; causal channels; mediation analysis; causal pathways; series logit estimation; nonparametric estimation; inverse probability weighting; propensity score (search for similar items in EconPapers)
JEL-codes: C21 (search for similar items in EconPapers)
Pages: 31 pages
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Journal Article: Nonparametric estimation of natural direct and indirect effects based on inverse probability weighting (2019)
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