Machine Learning Estimation of Heterogeneous Causal Effects: Empirical Monte Carlo Evidence

114 Pages Posted: 23 Jan 2019 Last revised: 24 Sep 2022

See all articles by Michael Knaus

Michael Knaus

University of St. Gallen

Michael Lechner

University of St. Gallen - Swiss Institute for Empirical Economic Research

Anthony Strittmatter

CREST-ENSAE

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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.

Keywords: causal forest, causal machine learning, random forest, conditional average treatment effects, lasso, selection-on-observables

JEL Classification: C21

Suggested Citation

Knaus, Michael and Lechner, Michael and Strittmatter, Anthony, Machine Learning Estimation of Heterogeneous Causal Effects: Empirical Monte Carlo Evidence. IZA Discussion Paper No. 12039, Available at SSRN: https://ssrn.com/abstract=3318814

Michael Knaus (Contact Author)

University of St. Gallen ( email )

Varnbuelstr. 14
Saint Gallen, St. Gallen CH-9000
Switzerland

Michael Lechner

University of St. Gallen - Swiss Institute for Empirical Economic Research ( email )

Varnbuelstrasse 14
St. Gallen, 9000
Switzerland
+41 71 224 2320 (Phone)

Anthony Strittmatter

CREST-ENSAE ( email )

France

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