What Is the Value Added by Using Causal Machine Learning Methods in a Welfare Experiment Evaluation?

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.

• Publication year

  2018

• Venue

  Labour Economics

• Publication date

  2018-12-16

• Fields of study

  Computer Science, Economics

• Identifiers
  DOI 10.1016/j.labeco.2023.102412arXiv 1812.06533
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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