A new GLO Discussion Paper suggests that causal machine learning methods provide support for theoretical labor supply predictions.
GLO Discussion Paper No. 336, 2019
What Is the Value Added by Using Causal Machine Learning Methods in a Welfare Experiment Evaluation? – Download PDF
by Strittmatter, Anthony
GLO Fellow Anthony Strittmatter
Author Abstract: 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.
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