Estimating Individual Treatment Effect in Observational Data Using Random Forest Methods
本文在反事实框架下利用随机森林方法直接建模响应，估计个体处理效应，发现反事实合成森林等方法在复杂异质环境中表现良好，并应用于Project Aware试验分析药物使用与性风险的关系。
treatments. Making use of random forests (RF) within the counterfactual framework we estimate individual treatment effects by directly modeling the response. We find that accurate estimation of individual treatment effects is possible even in complex heterogenous settings but that the type of RF approach plays an important role in accuracy. Methods designed to be adaptive to confounding, when used in parallel with out-of-sample estimation, do best. One method found to be especially promising is counterfactual synthetic forests. We illustrate this new methodology by applying it to a large comparative effectiveness trial, Project Aware, to explore the role drug use plays in sexual risk. The analysis reveals important connections between risky behavior, drug usage, and sexual risk.