Effect or Treatment Heterogeneity? Policy Evaluation with Aggregated and Disaggregated Treatments
Type
conference paper
Date Issued
2022
Author(s)
Abstract (De)
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.
Language
English
HSG Classification
contribution to scientific community
HSG Profile Area
None
Event Title
Workshop: Frontiers in Econometrics
Event Location
University of Berne
Event Date
12.-13.05.2022
Subject(s)
Division(s)
Eprints ID
268439
File(s)![Thumbnail Image]()
Name
P.Heiler_M.Knaus.pdf
Size
1.1 MB
Format
Adobe PDF
Checksum (MD5)
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