标题： 修改的因果森林 显示英文标题

标题： Modified Causal Forest

摘要： 揭示政策和商业决策在不同粒度层次上的因果效应的异质性，为决策者提供了重要价值。 本文通过在多个维度上修改因果森林方法（Wager和Athey，2018），在可观测变量选择框架下开发了多重处理模型的估计和推断程序。 新的估计量在因果效应的不同聚合层次上具有理想的理论、计算和实际性质。 虽然经验蒙特卡洛研究表明它们优于之前提出的估计量，但对一个积极劳动力市场计划的评估应用展示了它们在应用研究中的价值。 显示英文摘要

摘要： 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.