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

Michael Knaus, Michael Lechner and Anthony Strittmatter

No 12039, IZA Discussion Papers from Institute of Labor Economics (IZA)

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 (search for similar items in EconPapers)
JEL-codes: C21 (search for similar items in EconPapers)
Pages: 114 pages
Date: 2018-12
New Economics Papers: this item is included in nep-big and nep-cmp
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (30)

Published - published in: Econometrics Journal, 2021, 24 (1), 134-161

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Related works:
Journal Article: Machine learning estimation of heterogeneous causal effects: Empirical Monte Carlo evidence (2021)
Working Paper: Machine Learning Estimation of Heterogeneous Causal Effects: Empirical Monte Carlo Evidence (2018)
Working Paper: Machine Learning Estimation of Heterogeneous Causal Effects: Empirical Monte Carlo Evidence (2018)
Working Paper: Machine Learning Estimation of Heterogeneous Causal Effects: Empirical Monte Carlo Evidence (2018)
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