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:为了调查因果机制,因果调解分析将总处理效应分解为自然的直接和间接效应。本文件审查对一般处理效果模型中直接和间接效应的直接和间接效应的估计,在一般处理效果模型中,治疗可以是二元的、多价值的、连续的或混合的。我们提出普遍加权估计值,通过解决一系列扩大的方程来估计加权数。在某些充分的条件下,我们表明拟议的估计值是一致的,并且是非象征性的正常的。具体地说,如果治疗是分散的,拟议的估计值达到半对称效率界限。与此同时,在治疗持续进行的情况下,提议的估计值的趋同率低于$N ⁇ -1/2};然而,它们的效率仍然高于从真实加权函数中得出的数值。模拟研究表明,我们的估计值表现出令人满意的有限标值表现,而应用则表明其实际价值。