论文标题
通过一般治疗的非参数估计调解效应
Nonparametric Estimation of Mediation Effects with A General Treatment
论文作者
论文摘要
为了研究因果机制，因果介导分析将总治疗效应分解为自然直接和间接效应。本文研究了一般治疗效应模型中直接和间接效应的估计，在该模型中，该处理可以是二进制，多价值，连续或混合物。我们提出了通过求解扩展的方程组估算的权重的广义加权估计器。在某些足够的条件下，我们表明所提出的估计器是一致的，并且渐近地正常。具体而言，当治疗是离散的时，提出的估计器达到了半摩托效率的界限。同时，当治疗连续时，所提出的估计器的收敛速率慢于$ n^{ - 1/2} $;但是，它们仍然比真正的加权函数构建的效率更高。一项模拟研究表明，我们的估计器表现出令人满意的有限样本性能，而应用程序显示其实际价值
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
