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Recent studies have proposed causal machine learning (CML) methods toestimate conditional average treatment effects (CATEs). In this study, Iinvestigate whether CML methods add value compared to conventional CATEestimators by re-evaluating Connecticut's Jobs First welfare experiment. Thisexperiment entails a mix of positive and negative work incentives. Previousstudies show that it is hard to tackle the effect heterogeneity of Jobs Firstby means of CATEs. I report evidence that CML methods can provide support forthe theoretical labor supply predictions. Furthermore, I document reasons whysome conventional CATE estimators fail and discuss the limitations of CMLmethods.

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