Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: http://hdl.handle.net/10419/194352
Autoren: 
Strittmatter, Anthony
Datum: 
2019
Reihe/Nr.: 
GLO Discussion Paper 336
Zusammenfassung: 
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.
Schlagwörter: 
Labor supply, individualized treatment effects, conditional average treatment effects, random forest
JEL: 
H75
I38
J22
J31
C21
Dokumentart: 
Working Paper

Datei(en):
Datei
Größe
639.71 kB





Publikationen in EconStor sind urheberrechtlich geschützt.