Heterogeneous Treatment Effect Estimation under Noncompliance in the Illinois Workplace Wellness Study with Bayesian Tree Ensembles
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贝叶斯树集成估计不依从下的异质性效应：伊利诺伊职场健康研究
- 研究问题
- 随机试验存在不依从且结局为二元变量时，如何区分依从率变化与效应变化，并稳定估计依从者群体中的异质性处理效应？
- 方法与思路
- 作者提出适用于二元结局和不依从情形的贝叶斯因果森林，通过反复插补个体依从类型，灵活估计依从者处理效应。
- 材料陈述的贡献
- 摘要称，模拟支持方法在依从与效应均异质时的用途；职场健康试验应用发现部分代谢和健康安全感知结局存在效应异质性。
- 阅读关注点
- 应核对依从者效应的识别条件、潜在依从类型插补与先验设置，以及弱工具变量敏感性、异质性不确定性和随机种子稳定性。
- 阅读边界
- 这是预印本，同行评审及期刊接收未核实。摘要未给出识别假设和后验结果细节，也未证明方法在所有弱工具情形下稳定。
原文摘要
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.