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

Michael Knaus (), Michael Lechner and Anthony Strittmatter ()

No 13402, CEPR Discussion Papers from C.E.P.R. Discussion Papers

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; conditional average treatment effects; Lasso; Random Forest; selection-on-observables (search for similar items in EconPapers)
JEL-codes: C21 (search for similar items in EconPapers)
Date: 2018-12
New Economics Papers: this item is included in nep-big, nep-cmp and nep-pay
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Working Paper: Machine Learning Estimation of Heterogeneous Causal Effects: Empirical Monte Carlo Evidence (2018) Downloads
Working Paper: Machine Learning Estimation of Heterogeneous Causal Effects: Empirical Monte Carlo Evidence (2018) Downloads
Working Paper: Machine Learning Estimation of Heterogeneous Causal Effects: Empirical Monte Carlo Evidence (2018) Downloads
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