标题： 编译器随机直接效应：识别与稳健估计 显示英文标题

标题： Complier stochastic direct effects: identification and robust estimation

摘要： 中介分析对于理解暴露-结果关系背后的机制至关重要。 在本文中，我们通过工具变量（IV）的随机化来识别暴露对结果的直接效应，而不是通过中介变量。 据我们所知，这种估计量之前尚未被考虑或估计。 我们提出了几种针对该估计量的估计器：逆概率治疗加权估计器（IPTW）的比率、估计方程估计器（EE）的比率、目标最小损失基础估计器（TMLE）的比率，以及直接针对CSDE的目标TMLE。 这些估计器适用于多种研究设计，包括随机鼓励试验，如我们作为示例考虑的MTO住房券实验，治疗中断情况和孟德尔随机化。 我们发现IPTW估计器对有限样本偏差最为敏感，在样本量为N=100的情况下，即使所有模型都正确指定，偏差仍超过40%。 相比之下，EE估计器和兼容的TMLE估计器对有限样本的敏感性要低得多。 EE和TMLE估计器在效率方面以及减少对正确参数模型规范的依赖方面也优于IPTW估计器。 显示英文摘要

摘要： Mediation analysis is critical to understanding the mechanisms underlying exposure-outcome relationships. In this paper, we identify the instrumental variable (IV)-direct effect of the exposure on the outcome not through the mediator, using randomization of the instrument. To our knowledge, such an estimand has not previously been considered or estimated. We propose and evaluate several estimators for this estimand: a ratio of inverse-probability of treatment-weighted estimators (IPTW), a ratio of estimating equation estimators (EE), a ratio of targeted minimum loss-based estimators (TMLE), and a TMLE that targets the CSDE directly. These estimators are applicable for a variety of study designs, including randomized encouragement trials, like the MTO housing voucher experiment we consider as an illustrative example, treatment discontinuities, and Mendelian randomization. We found the IPTW estimator to be the most sensitive to finite sample bias, resulting in bias of over 40% even when all models were correctly specified in a sample size of N=100. In contrast, the EE estimator and compatible TMLE estimator were far less sensitive to finite samples. The EE and TMLE estimators also have advantages over the IPTW estimator in terms of efficiency and reduced reliance on correct parametric model specification.