Using Causal Forests to Predict Treatment Heterogeneity: An Application to Summer Jobs
以青年暑期工作项目为例，详细解释了因果森林算法如何基于可观测变量预测处理效应异质性，并通过保留样本检验预测效果，发现该方法能识别标准交互方法遗漏的异质性。
To estimate treatment heterogeneity in two randomized controlled trials of a youth summer jobs program, we implement Wager and Athey's (2015) causal forest algorithm. We provide a step-by-step explanation targeted at applied researchers of how the algorithm predicts treatment effects based on observables. We then explore how useful the predicted heterogeneity is in practice by testing whether youth with larger predicted treatment effects actually respond more in a hold-out sample. Our application highlights some limitations of the causal forest, but it also suggests that the method can identify treatment heterogeneity for some outcomes that more standard interaction approaches would have missed.