CATE满足ML--条件平均处理效应和机器学习
CATE meets ML -- The Conditional Average Treatment Effect and Machine Learning
摘要：
对于处理效应--现代计量经济学分析的核心问题之一--预测和估计是一枚硬币的两个方面。事实证明，机器学习方法是广义预测模型的工具。结合计量经济学理论，我们不仅可以估计平均值，还可以估计个性化的治疗效果--条件平均处理效应（CATE）。在本教程中，我们概述了新颖的方法，详细解释了它们，并通过Quantlets将它们应用到实际数据应用程序中。我们研究了小额信贷的可用性对借款金额的影响，以及401（k）养老金计划资格是否对净金融资产产生影响，作为两个实证例子。所提出的方法工具箱包含元学习器，如Doubly-Robust，R-，T-和X-学习器，以及专门设计用于估计CATE的方法，如因果BART和广义随机森林。在小额信贷和401（k）的例子中，我们发现所有观察结果都有积极的处理效应，但处理效应异质性的证据相互矛盾。一个额外的模拟研究，其中真正的处理效应是已知的，使我们能够比较不同的方法，并观察模式和相似性。
英文摘要：
For treatment effects - one of the core issues in modern econometric analysis - prediction and estimation are two sides of the same coin. As it turns out, machine learning methods are the tool for generalized prediction models. Combined with econometric theory, they allow us to estimate not only the average but a personalized treatment effect - the conditional average treatment effect (CATE). In this tutorial, we give an overview of novel methods, explain them in detail, and apply them via Quantlets in real data applications. We study the effect that microcredit availability has on the amount of money borrowed and if 401(k) pension plan eligibility has an impact on net financial assets, as two empirical examples. The presented toolbox of methods contains meta-learners, like the Doubly-Robust, R-, T- and X-learner, and methods that are specially designed to estimate the CATE like the causal BART and the generalized random forest. In both, the microcredit and 401(k) example, we find a positive treatment effect for all observations but conflicting evidence of treatment effect heterogeneity. An additional simulation study, where the true treatment effect is known, allows us to compare the different methods and to observe patterns and similarities.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
