Finite-sample bias-variance tradeoff with variables related to trial participation inserted into causal forest models for ensuring generalizability
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因果森林纳入试验参与变量时的有限样本偏差—方差权衡
- 研究问题
- 将随机试验中的条件平均处理效应推广至来源总体时，直接加入试验参与相关变量，会怎样影响有限样本偏差和方差？
- 方法与思路
- 作者通过模拟比较因果森林纳入参与变量和逆概率加权等策略，并用欧米伽三脂肪酸与冠心病随机试验说明估计变化。
- 材料陈述的贡献
- 摘要称，在所设模拟中，高维参与变量带来的方差增加常超过偏差改善，而逆概率加权在比较场景中持续改善表现。
- 阅读关注点
- 应核对来源总体与试验样本的生成机制、效应异质性设定、权重估计方式，以及不同样本量下偏差和精度的比较。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未获核实；超过三个变量的精度下降属于所设模拟，不能视为通用阈值。
原文摘要
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.