Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/193333 
Year of Publication: 
2018
Series/Report no.: 
IZA Discussion Papers No. 12039
Publisher: 
Institute of Labor Economics (IZA), Bonn
Abstract: 
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.
Subjects: 
causal machine learning
conditional average treatment effects
selection-on-observables
random forest
causal forest
lasso
JEL: 
C21
Document Type: 
Working Paper

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