发帖
雷达卡
摘要翻译：
最近的研究提出了因果机器学习(CML)方法来估计条件平均治疗效果（CATEs）。在这项研究中，我通过重新评估康涅狄格州的工作优先福利实验，研究CML方法是否比传统的CATE估计方法增加价值。这个实验需要积极和消极的工作激励。以往的研究表明，用CATES方法很难首先解决就业效应的异质性问题。我报告了CML方法可以为理论劳动供给预测提供支持的证据。此外，我还记录了一些传统的CATE估计器失败的原因，并讨论了CML方法的局限性。
---
英文标题：
《What Is the Value Added by Using Causal Machine Learning Methods in a
Welfare Experiment Evaluation?》
---
作者：
Anthony Strittmatter
---
最新提交年份：
2019
---
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。
--
---
英文摘要：
Recent studies have proposed causal machine learning (CML) methods to estimate conditional average treatment effects (CATEs). In this study, I investigate whether CML methods add value compared to conventional CATE estimators by re-evaluating Connecticut's Jobs First welfare experiment. This experiment entails a mix of positive and negative work incentives. Previous studies show that it is hard to tackle the effect heterogeneity of Jobs First by means of CATEs. I report evidence that CML methods can provide support for the theoretical labor supply predictions. Furthermore, I document reasons why some conventional CATE estimators fail and discuss the limitations of CML methods.
---
PDF链接：
https://arxiv.org/pdf/1812.06533
京公网安备 11010802022788号
