Direct and indirect effects of continuous treatments based on generalized propensity score weighting
提出半参数和非参数方法，利用广义倾向得分加权将连续处理变量的总因果效应分解为直接效应和通过中介变量传导的间接效应，并给出估计量的渐近正态性证明。
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.