Titelaufnahme

Titel
Modified causal forests for estimating heterogeneous causal effects / Michael Lechner (SEW at University St. Gallen, CEPR, CESifo, IAB, IZA and RWI) ; IZA Institute of Labor Economics
VerfasserLechner, Michael
KörperschaftForschungsinstitut zur Zukunft der Arbeit
ErschienenBonn, Germany : IZA Institute of Labor Economics, December 2018
Ausgabe
Elektronische Ressource
Umfang1 Online-Ressource (62 Seiten) : Diagramme
SerieDiscussion paper ; no. 12040
URNurn:nbn:de:hbz:5:2-175513 
Zugriffsbeschränkung
 Das Dokument ist öffentlich zugänglich im Rahmen des deutschen Urheberrechts.
Volltexte
Modified causal forests for estimating heterogeneous causal effects [1.13 mb]
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Zusammenfassung (Englisch)

Uncovering the heterogeneity of causal effects of policies and business decisions at various levels of granularity provides substantial value to decision makers. This paper develops new estimation and inference procedures for multiple treatment models in a selection-onobservables frame-work by modifying the Causal Forest approach suggested by Wager and Athey (2018). The new estimators have desirable theoretical and computational properties for various aggregation levels of the causal effects. An Empirical Monte Carlo study shows that they may outperform previously suggested estimators. Inference tends to be accurate for effects relating to larger groups and conservative for effects relating to fine levels of granularity. An application to the evaluation of an active labour market programme shows the value of the new methods for applied research.