Direct and indirect effects of continuous treatments based on generalized propensity score weighting

Martin Huber, Yu-Chin Hsu, Ying‐Ying Lee and Layal Lettry

Journal of Applied Econometrics, 2020, vol. 35, issue 7, 814-840

Abstract: This paper proposes semi‐ and nonparametric methods for disentangling the total causal effect of a continuous treatment on an outcome variable into its natural direct effect and the indirect effect that operates through one or several intermediate variables called mediators jointly. Our approach is based on weighting observations by the inverse of two versions of the generalized propensity score (GPS), namely the conditional density of treatment either given observed covariates or given covariates and the mediator. Our effect estimators are shown to be asymptotically normal when the GPS is estimated by either a parametric or a nonparametric kernel‐based method. We also provide a simulation study and an empirical illustration based on the Job Corps experimental study.

Date: 2020
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Citations: View citations in EconPapers (8)

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https://doi.org/10.1002/jae.2765

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Working Paper: Direct and indirect effects of continuous treatments based on generalized propensity score weighting (2018)
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