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Modified Causal Forests for Estimating Heterogeneous Causal Effects

Michael Lechner

No 1901, Economics Working Paper Series from University of St. Gallen, School of Economics and Political Science

Abstract: Uncovering the heterogeneity of causal effects of policies and business decisions at various levels of granularity provides substantial value to decision makers. This paper develops new estimation and inference procedures for multiple treatment models in a selection-on-observables framework by modifying the Causal Forest approach suggested by Wager and Athey (2018). The new estimators have desirable theoretical and computational properties for various aggregation levels of the causal effects. An Empirical Monte Carlo study shows that they may outperform previously suggested estimators. Inference tends to be accurate for effects relating to larger groups and conservative for effects relating to fine levels of granularity. An application to the evaluation of an active labour market programme shows the value of the new methods for applied research.

Keywords: Causal machine learning; statistical learning; average treatment effects; conditional average treatment effects; multiple treatments; selection-on-observable; causal forests (search for similar items in EconPapers)
JEL-codes: C21 J68 (search for similar items in EconPapers)
Pages: 64 pages
Date: 2019-01
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ecm and nep-lab
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http://ux-tauri.unisg.ch/RePEc/usg/econwp/EWP-1901.pdf (application/pdf)

Related works:
Working Paper: Modified Causal Forests for Estimating Heterogeneous Causal Effects (2019) Downloads
Working Paper: Modified Causal Forests for Estimating Heterogeneous Causal Effects (2019) Downloads
Working Paper: Modified Causal Forests for Estimating Heterogeneous Causal Effects (2018) Downloads
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