Hsu, Yu-Chin Huber, Martin Lee, Ying-Ying Pipoz, Layal 2018-06-01 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. application/pdf https://folia.unifr.ch/global/documents/306658 https://folia.unifr.ch/documents/306658/files/WP_SES_495.pdf eng http://www.unifr.ch/ses/wp info:eu-repo/semantics/openAccess License undefined Mediation direct and indirect effects continuous treatment weighting generalized propensity score info:eu-repo/classification/udc/33 Direct and indirect effects of continuous treatments based on generalized propensity score weighting http://purl.org/coar/resource_type/c_18ws