伊利诺伊州工作场所健康研究中存在不依从情况时的异质性处理效应估计：基于贝叶斯树集成方法
Heterogeneous Treatment Effect Estimation under Noncompliance in the Illinois Workplace Wellness Study with Bayesian Tree Ensembles
AI总结：
针对存在不依从情况的随机试验，提出贝叶斯因果森林模型以估计依从者的异质性处理效应，通过模拟验证方法有效性，并将其应用于伊利诺伊州工作场所健康研究，发现了干预对代谢参数和健康安全感知的异质性效应。
摘要：
在存在不依从情况的随机试验中估计可变处理效应本质上具有挑战性，因为变异来源于两个独立部分：处理效应本身的变异和依从率的变异。在这种情况下，现有的灵活机器学习方法对弱工具变量问题敏感，且在使用不同初始化或随机种子重复运行时，可能产生不稳定的异质性估计。我们的主要方法学贡献是提出一种适用于二值响应变量、存在不依从情况场景的贝叶斯因果森林模型。通过多次插补个体的依从类型，我们可以灵活估计依从者中的异质性处理效应。模拟研究表明，当依从性和处理效应存在异质性时，我们的方法具有实用性。我们使用该方法检测并分析伊利诺伊州工作场所健康研究中的处理效应异质性，该研究不仅具有异质性且偏向单侧的依从性，还包含多个感兴趣的二值结局。我们聚焦于干预后一年的三个结局：确认干预对慢性病存在的效应为零；在经典偏效应估计中平均效应为零的情况下，发现干预对代谢参数的影响存在有意义的异质性；还发现干预对个体感知管理层优先考虑健康与安全的程度存在异质性。
英文摘要：
Estimating varying treatment effects in randomized trials with noncompliance is inherently challenging since variation comes from two separate sources: variation in the impact itself and variation in the compliance rate. In this setting, existing flexible machine learning methods are sensitive to the weak instruments problem and can yield unstable estimates of heterogeneity when run repeatedly with different initialization or random seeds. Our main methodological contribution is to present a Bayesian Causal Forest model for binary response variables in scenarios with noncompliance. By repeatedly imputing individuals' compliance types, we can flexibly estimate heterogeneous treatment effects among compliers. Simulation studies demonstrate the usefulness of our approach when compliance and treatment effects are heterogeneous. We use this method to detect and analyze heterogeneity in the treatment effects in the Illinois Workplace Wellness Study, which not only features heterogeneous and one-sided compliance but also several binary outcomes of interest. We focus on three outcomes one year after intervention. We confirm a null effect on the presence of a chronic condition, discover meaningful heterogeneity in the impact of the intervention on metabolic parameters though the average effect is null in classical partial effect estimates, and find heterogeneity in the intervention's effect on individuals' perception of management prioritization of health and safety.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
