Direct and indirect effects of continuous treatments based on generalized propensity score weighting
Ying-Ying Lee () and
Layal Pipoz ()
No 495, FSES Working Papers from Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland
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 or mediators. 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 application to the Job Corps program.
Keywords: Mediation; direct and indirect effects; continuous treatment; weighting; generalized propensity score (search for similar items in EconPapers)
JEL-codes: C21 (search for similar items in EconPapers)
Pages: 27 pages
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Journal Article: Direct and indirect effects of continuous treatments based on generalized propensity score weighting (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:fri:fribow:fribow00495
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