What Is the Value Added by Using Causal Machine Learning Methods in a Welfare Experiment Evaluation?

Research Seminars

Recent studies have proposed causal machine learning (CML) methods to estimate conditional average treatment e ffects (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 eff ect 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.

Veranstaltungsort

ZEW – Leibniz-Zentrum für Europäische Wirtschaftsforschung

Personen

Prof. Anthony Strittmatter Ph.D.

Anthony Strittmatter // Universität St. Gallen, Schweiz

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ZEW – Leibniz-Zentrum für Europäische Wirtschaftsforschung

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