摘要翻译：
在不同粒度级别上揭示策略和业务决策的因果影响的异构性为决策者提供了很大的价值。本文通过对Wager和Athe(2018)提出的因果森林方法的几个维度的修正，在一个可观察到的选择框架中为多个处理模型开发了新的估计和推断程序。新的估计量对于因果效应的不同聚集水平具有理想的理论、计算和实用性质。虽然一项实证蒙特卡洛研究表明，它们的表现优于以前建议的估计值，但一项对活跃劳动力市场方案的评估的应用表明了新方法在应用研究中的价值。
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英文标题：
《Modified Causal Forests for Estimating Heterogeneous Causal Effects》
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作者：
Michael Lechner
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最新提交年份：
2019
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分类信息：

一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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一级分类：Statistics        统计学
二级分类：Machine Learning        机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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英文摘要：
  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 new estimation and inference procedures for multiple treatment models in a selection-on-observables framework by modifying the Causal Forest approach suggested by 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 programme shows the value of the new methods for applied research.
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PDF链接：
https://arxiv.org/pdf/1812.09487