Nonparametric estimation of mediation effects with a general treatment
提出一种广义加权估计方法，用于估计一般处理变量（二值、多值、连续或混合）下的直接和间接中介效应，并证明了估计量的一致性和渐近正态性。
.To investigate causal mechanisms, causal mediation analysis decomposes the total treatment effect into the natural direct and indirect effects. This article examines the estimation of the direct and indirect effects in a general treatment effect model, where the treatment can be binary, multi-valued, continuous, or a mixture. We propose generalized weighting estimators with weights estimated by solving an expanding set of equations. Under some sufficient conditions, we show that the proposed estimators are consistent and asymptotically normal. Specifically, when the treatment is discrete, the proposed estimators attain semiparametric efficiency bounds. Meanwhile, when the treatment is continuous, the convergence rates of the proposed estimators are slower than N−1/2; however, they are still more efficient than those constructed from the true weighting function. A simulation study reveals that our estimators exhibit satisfactory finite-sample performance, while an application shows their practical value.