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Anthony STRITTMATTER (University of St. Gallen) "What Is the Value Added by Using Causal Machine Learning Methods in a Welfare Experiment Evaluation?"
Job market interview
Time: 12:30pm – 13:45 pm
Date: 23th of January 2020
Place: Room 3001
Anthony STRITTMATTER (University of St. Gallen) “What Is the Value Added by Using Causal Machine Learning Methods in a Welfare Experiment Evaluation?”
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.