  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.
