Modified Causal Forests for Estimating Heterogeneous Causal Effects

Michael Lechner

No 13430, CEPR Discussion Papers from C.E.P.R. Discussion Papers

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 esti-mators 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 mar-ket programme shows the value of the new methods for applied research.

Keywords: Causal machine learning; Statistical learning; Average treatment effects; Conditional aver-age 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)
Date: 2019-01
New Economics Papers: this item is included in nep-big, nep-cmp and nep-lab
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Citations: View citations in EconPapers (13)

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