Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/265801 
Year of Publication: 
2022
Series/Report no.: 
IZA Discussion Papers No. 15580
Publisher: 
Institute of Labor Economics (IZA), Bonn
Abstract: 
Binary treatments are often ex-post aggregates of multiple treatments or can be disaggregated into multiple treatment versions. Thus, effects can be heterogeneous due to either effect or treatment heterogeneity. We propose a decomposition method that uncovers masked heterogeneity, avoids spurious discoveries, and evaluates treatment assignment quality. The estimation and inference procedure based on double/debiased machine learning allows for high-dimensional confounding, many treatments and extreme propensity scores. Our applications suggest that heterogeneous effects of smoking on birthweight are partially due to different smoking intensities and that gender gaps in Job Corps effectiveness are largely explained by differences in vocational training.
Subjects: 
causal inference
causal machine learning
double machine learning
heterogeneous treatment effects
overlap
treatment versions
JEL: 
C14
C21
Document Type: 
Working Paper

Files in This Item:
File
Size
2.02 MB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.