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Journal of Econometric Methods

Ed. by Giacomini, Raffaella / Li, Tong


Mathematical Citation Quotient (MCQ) 2018: 0.06

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2156-6674
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Nonparametric estimation of natural direct and indirect effects based on inverse probability weighting

Yu-Chin Hsu / Martin Huber / Tsung-Chih Lai
Published Online: 2018-06-13 | DOI: https://doi.org/10.1515/jem-2017-0016

Abstract

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 channels; causal mechanisms; causal pathways; direct effects; indirect effects; inverse probability weighting; mediation analysis; nonparametric estimation; propensity score; series logit estimation

JEL Classification: C21

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About the article

Published Online: 2018-06-13


Citation Information: Journal of Econometric Methods, Volume 8, Issue 1, 20170016, ISSN (Online) 2156-6674, DOI: https://doi.org/10.1515/jem-2017-0016.

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