What Is the Value Added by Using Causal Machine Learning Methods in a Welfare Experiment Evaluation?
Type
journal article
Author(s)
Abstract (De)
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.
Language
English
HSG Classification
contribution to scientific community
HSG Profile Area
SEPS - Quantitative Economic Methods
Refereed
No
Subject(s)
Division(s)
Eprints ID
256787
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Name
paper_20190316.pdf
Size
639.71 KB
Format
Adobe PDF
Checksum (MD5)
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