What Is the Value Added by Using Causal Machine Learning Methods in a Welfare Experiment Evaluation?
Anthony Strittmatter ()
No 336, GLO Discussion Paper Series from Global Labor Organization (GLO)
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.
Keywords: Labor supply; individualized treatment effects; conditional average treatment effects; random forest (search for similar items in EconPapers)
JEL-codes: H75 I38 J22 J31 C21 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-big, nep-cmp, nep-exp and nep-lma
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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:zbw:glodps:336
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