To investigate causal mechanisms, causal mediation analysis decomposes the total treatment effect into the natural direct and indirect effects. This paper 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 the 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 that constructed from the true weighting function. A simulation study reveals that our estimators exhibit a satisfactory finite-sample performance, while an application shows their practical value

翻译：为探究因果机制，因果中介分析将总处理效应分解为自然直接效应与间接效应。本文研究一般处理效应模型中直接效应与间接效应的估计问题，其中处理变量可为二元、多值、连续或混合型。我们提出广义加权估计量，其权重通过求解一组扩展方程得到。在充分条件约束下，证明所提估计量具有一致性与渐近正态性。具体而言，当处理变量为离散型时，所提估计量达到半参数效率界；当处理变量为连续型时，虽收敛速度低于$N^{-1/2}$，但较基于真实权重函数的估计量更高效。模拟研究表明，本文估计量展现出良好的有限样本表现，而应用实例验证了其实用价值。