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Nonparametric estimation of natural direct and indirect effects based on inverse probability weighting

  • Huber, Martin Department of Economics, Université de Fribourg, Fribourg, Switzerland
  • Yu-Chin, Hsu Institute of Economics, Academia Sinica, Taipei, Taiwan
  • Tsung-Chih, Lai Department of Economics, Feng Chia University, Taichung, Taiwan
    2019
Published in:
  • Journal of Econometric Methods. - 2019, vol. 8, no. 1, p. 1-20
Causal channels
Causal mechanisms
Causal pathways
Direct effects
Indirect effects
Inverse probability weighting
Mediation analysis
Nonparametric estimation
Propensity score
Series logit estimation
C21
English 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.
Faculty
Faculté des sciences économiques et sociales et du management
Department
Département d'économie politique
Language
  • English
Classification
Economics
License
License undefined
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Persistent URL
https://folia.unifr.ch/unifr/documents/309103
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