What is the value added by using causal machine learning methods in a welfare experiment evaluation?

Anthony Strittmatter

Labour Economics, 2023, vol. 84, issue C

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: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:labeco:v:84:y:2023:i:c:s0927537123000878

DOI: 10.1016/j.labeco.2023.102412

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