Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/193334 
Autor:innen: 
Erscheinungsjahr: 
2018
Schriftenreihe/Nr.: 
IZA Discussion Papers No. 12040
Verlag: 
Institute of Labor Economics (IZA), Bonn
Zusammenfassung: 
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-on-observables 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.
Schlagwörter: 
causal machine learning
statistical learning
average treatment effects
conditional average treatment effects
multiple treatments
selection-on-observable
causal forests
JEL: 
C21
J68
Dokumentart: 
Working Paper

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