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 the presented study, it is investigated 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. Evidence is reported, that CML methods can provide support for the theoretical labor supply predictions. Furthermore, reasons are documented why some conventional CATE estimators fail and discuss the limitations of CML methods.