What Is the Value Added by Using Causal Machine Learning Methods in a Welfare Experiment Evaluation?
Econometrics
2021-12-07 v3
Abstract
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.
Cite
@article{arxiv.1812.06533,
title = {What Is the Value Added by Using Causal Machine Learning Methods in a Welfare Experiment Evaluation?},
author = {Anthony Strittmatter},
journal= {arXiv preprint arXiv:1812.06533},
year = {2021}
}