What is the Value Added by using Causal Machine Learning Methods in a Welfare Experiment Evaluation?

Anthony Strittmatter

VfS Annual Conference 2019 (Leipzig): 30 Years after the Fall of the Berlin Wall - Democracy and Market Economy from Verein für Socialpolitik / German Economic Association

Abstract: I investigate causal machine learning (CML) methods to estimate effect heterogeneity by means of conditional average treatment effects (CATEs). In particular, I study whether the estimated effect heterogeneity can provide evidence for the theoretical labour supply predictions of Connecticut's Jobs First welfare experiment. For this application, Bitler, Gelbach, and Hoynes (2017) show that standard CATE estimators fail to provide evidence for theoretical labour supply predictions. Therefore, this is an interesting benchmark to showcase the value added by using CML methods. I report evidence that the CML estimates of CATEs provide support for the theoretical labour supply predictions. Furthermore, I document some reasons why standard CATE estimators fail to provide evidence for the theoretical predictions. However, I show the limitations of CML methods that prevent them from identifying all the effect heterogeneity of Jobs First.

Keywords: Labour 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 and nep-cmp
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/10419/203499/1/VfS-2019-pid-25713.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:vfsc19:203499

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