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)

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.

Keywords: Labor supply; individualized treatment effects; conditional average treatment effects; random forest (search for similar items in EconPapers)
JEL-codes: C21 H75 I38 J22 J31 (search for similar items in EconPapers)
Date: 2019
New Economics Papers: this item is included in nep-big, nep-cmp, nep-exp and nep-lma
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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https://www.econstor.eu/bitstream/10419/194352/1/GLO-DP-0336.pdf (application/pdf)

Related works:
Working Paper: What Is the Value Added by Using Causal Machine Learning Methods in a Welfare Experiment Evaluation? (2021)
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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