Titelaufnahme

Titel
Machine learning estimation of heterogeneous causal effects : empirical Monte Carlo evidence / Michael C. Knaus (University of St. Gallen and IZA), Michael Lechner (University of St. Gallen, CEPR, CESifo, IAB and IZA), Anthony Strittmatter (University of St. Gallen) ; IZA Institute of Labor Economics
VerfasserKnaus, Michael C. ; Lechner, Michael ; Strittmatter, Anthony
KörperschaftForschungsinstitut zur Zukunft der Arbeit
ErschienenBonn, Germany : IZA Institute of Labor Economics, December 2018
Ausgabe
Elektronische Ressource
Umfang1 Online-Ressource (112 Seiten)
SerieDiscussion paper ; no. 12039
URNurn:nbn:de:hbz:5:2-174861 
Zugriffsbeschränkung
 Das Dokument ist öffentlich zugänglich im Rahmen des deutschen Urheberrechts.
Volltexte
Machine learning estimation of heterogeneous causal effects [1.26 mb]
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Zusammenfassung (Englisch)

We investigate the finite sample performance of causal machine learning estimators for heterogeneous causal effects at different aggregation levels. We employ an Empirical Monte Carlo Study that relies on arguably realistic data generation processes (DGPs) based on actual data. We consider 24 different DGPs, eleven different causal machine learning estimators, and three aggregation levels of the estimated effects. In the main DGPs, we allow for selection into treatment based on a rich set of observable covariates. We provide evidence that the estimators can be categorized into three groups. The first group performs consistently well across all DGPs and aggregation levels. These estimators have multiple steps to account for the selection into the treatment and the outcome process. The second group shows competitive performance only for particular DGPs. The third group is clearly outperformed by the other estimators.