Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/203499
Authors: 
Strittmatter, Anthony
Year of Publication: 
2019
Series/Report no.: 
Beiträge zur Jahrestagung des Vereins für Socialpolitik 2019: 30 Jahre Mauerfall - Demokratie und Marktwirtschaft - Session: Labor Economics VIII No. E09-V1
Abstract: 
I investigate causal machine learning (CML) methods to estimate effect heterogeneity by means of conditional average treatment effects (CATEs). In particular, I study whether the estimated effect heterogeneity can provide evidence for the theoretical labour supply predictions of Connecticut's Jobs First welfare experiment. For this application, Bitler, Gelbach, and Hoynes (2017) show that standard CATE estimators fail to provide evidence for theoretical labour supply predictions. Therefore, this is an interesting benchmark to showcase the value added by using CML methods. I report evidence that the CML estimates of CATEs provide support for the theoretical labour supply predictions. Furthermore, I document some reasons why standard CATE estimators fail to provide evidence for the theoretical predictions. However, I show the limitations of CML methods that prevent them from identifying all the effect heterogeneity of Jobs First.
Subjects: 
Labour supply
individualized treatment effects
conditional average treatment effects
random forest
JEL: 
H75
I38
J22
J31
C21
Document Type: 
Conference Paper

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