Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/194352
Authors: 
Strittmatter, Anthony
Year of Publication: 
2019
Series/Report no.: 
GLO Discussion Paper 336
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.
Subjects: 
Labor supply, individualized treatment effects, conditional average treatment effects, random forest
JEL: 
H75
I38
J22
J31
C21
Document Type: 
Working Paper

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