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DTSTART;TZID=Europe/Helsinki:20200123T123000
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SUMMARY:Anthony STRITTMATTER (University of St. Gallen) "What Is the Value Added by Using Causal Machine Learning Methods in a Welfare Experiment Evaluation?"
DESCRIPTION:Job market interview\nTime: 12:30pm – 13:45 pm\nDate: 23th of January 2020\nPlace: Room 3001\nAnthony STRITTMATTER (University of St. Gallen) “What Is the Value Added by Using Causal Machine Learning Methods in a Welfare Experiment Evaluation?”\nAbstract : 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.\n  \n
URL:https://crest.science/event/anthony-strittmatter/
CATEGORIES:Economics
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