What Is the Value Added by Using Causal Machine Learning Methods in a Welfare Experiment Evaluation?
Type
conference speech
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
Event Title
CSS Workshop
Event Location
Zürich
Event Date
23.05.2019
Subject(s)
Division(s)
Eprints ID
259145