What Is the Value Added by Using Causal Machine Learning Methods in a Welfare Experiment Evaluation?
Anthony Strittmatter ()
Papers from arXiv.org
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.
Date: 2018-12, Revised 2019-03
New Economics Papers: this item is included in nep-big, nep-cmp and nep-exp
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Working Paper: What Is the Value Added by Using Causal Machine Learning Methods in a Welfare Experiment Evaluation? (2019)
Working Paper: What is the Value Added by using Causal Machine Learning Methods in a Welfare Experiment Evaluation? (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1812.06533
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