Estimating conditional average treatment effects (CATE) from randomized controlled trials (RCTs) and generalizing them to broader populations is essential for personalizing treatment rules but is complicated by selection bias due to trial participation and potentially high dimensional covariates. We evaluated finite sample bias variance tradeoff for Causal Forest based CATE estimation strategies to address the selection bias. Identification theory suggests unbiased CATE estimation is possible when covariates related to trial participation are included in CATE estimating models. However, simulation studies demonstrated that, under realistic RCT sample sizes, variance inflation from high dimensional covariates often outweighed modest bias reduction. In our data generating process that define individual treatment effect (ITE) in source population and selected trial samples, including more than 3 covariates related to participation in causal forest substantially degraded precision unless sample sizes were large. In contrast, inverse probability weighting (IPW) based methods consistently improved performance across scenarios. Application to a RCT of omega 3 fatty acids and coronary heart disease illustrated how IPW shifts CATE estimates toward source population effects and refines heterogeneity assessments. Our findings highlight that including trial-selection variables for CATE estimating models may inflate estimator variance and reduce ITE prediction performance in applications using medical RCTs. Addressing selection bias separately (e.g. through IPW) would be a reasonable strategy.

翻译：评估随机对照试验（RCTs）的条件平均处理效应（CATE）并将其推广至更广泛人群，对制定个性化治疗方案至关重要，但试验参与导致的样本选择偏差及潜在的高维协变量使这一过程复杂化。本研究基于因果森林方法评估CATE估计策略中的有限样本偏差-方差权衡，以应对样本选择偏差。识别理论表明，当CATE估计模型纳入与试验参与相关的协变量时，可实现CATE的无偏估计。然而，模拟实验显示，在实际RCT样本量条件下，高维协变量引发的方差膨胀通常超过其带来的适度偏差减少。在定义源人群及选定试验样本个体处理效应（ITE）的数据生成过程中，除非样本量足够大，否则在因果森林中纳入超过3个与参与相关的协变量会显著降低精度。相比之下，基于逆概率加权（IPW）的方法在所有场景下均能持续提升性能。将方法应用于一项关于omega-3脂肪酸与冠心病的RCT时，结果揭示了IPW如何将CATE估计值向源人群效应偏移，并优化异质性评估。本研究发现表明，在CATE估计模型中纳入试验选择变量可能放大估计量方差，并降低基于医学RCT应用中的ITE预测性能。通过IPW等方法单独处理选择偏差将是合理策略。