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

Item Type Presentation
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.
Authors Knaus, Michael
Language English
Subjects economics
HSG Classification contribution to scientific community
HSG Profile Area SEPS - Quantitative Economic Methods
Date 26 June 2019
Event Title IAAE Annual conference
Event Location Nicosia, Zyprus
Event Dates 24. - 28.06.2019
Depositing User Prof. Ph.D Michael Knaus
Date Deposited 17 Dec 2019 11:15
Last Modified 20 Mar 2023 01:26


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Knaus, Michael: Machine Learning Estimation of Heterogeneous Causal Effects: Empirical Monte Carlo Evidence. IAAE Annual conference. Nicosia, Zyprus, 26 June 2019.

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