Modified Causal Forests for Estimating Heterogeneous Causal Effects

Michael Lechner

Papers from arXiv.org

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) in several dimensions. The new estimators have desirable theoretical, computational and practical properties for various aggregation levels of the causal effects. While an Empirical Monte Carlo study suggests that they outperform previously suggested estimators, an application to the evaluation of an active labour market programme shows the value of the new methods for applied research.

Date: 2018-12, Revised 2019-07
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Citations: View citations in EconPapers (13)

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http://arxiv.org/pdf/1812.09487 Latest version (application/pdf)

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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