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Direct and indirect effects of continuous treatments based on generalized propensity score weighting
Author(s) -
Huber Martin,
Hsu YuChin,
Lee YingYing,
Lettry Layal
Publication year - 2020
Publication title -
journal of applied econometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.878
H-Index - 99
eISSN - 1099-1255
pISSN - 0883-7252
DOI - 10.1002/jae.2765
Subject(s) - covariate , nonparametric statistics , propensity score matching , weighting , estimator , econometrics , inverse probability weighting , kernel (algebra) , outcome (game theory) , statistics , mathematics , parametric statistics , medicine , mathematical economics , combinatorics , radiology
Summary 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.