What Is the Value Added by Using Causal Machine Learning Methods in a Welfare Experiment Evaluation? - Anthony Strittmatter (Global Labor Organization (GLO), 2019)
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: H75; I38; J22; J31; C21; 330; Labor supply; individualized treatment effects; conditional average treatment effects; random forest