论文标题
修改的因果林
Modified Causal Forest
论文作者
论文摘要
揭示政策和业务决策在不同层面上的因果关系的异质性，对决策者提供了实质性的价值。本文通过在几个维度上修改因果森林方法（Wager and Athey，2018）来开发多种治疗模型的估计和推理程序。新估计器具有因果效应的各种聚合水平的理论，计算和实际特性。虽然一项经验性的蒙特卡洛研究表明，他们的表现先前提出了估计量，但在评估主动劳动力市场亲格拉姆（Pro-Gramme）的应用中显示了其对应用研究的价值。
Uncovering the heterogeneity of causal effects of policies and business decisions at various levels of granularity provides substantial value to decision makers. This paper develops estimation and inference procedures for multiple treatment models in a selection-on-observed-variables framework by modifying the Causal Forest approach (Wager and Athey, 2018) in several dimensions. The new estimators have desirable theoretical, computational, and practical properties for various aggregation levels of the causal effects. While an Empirical Monte Carlo study suggests that they outperform previously suggested estimators, an application to the evaluation of an active labour market pro-gramme shows their value for applied research.
