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Modified Causal Forests for Estimating Heterogeneous Causal Effects
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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.
Forward citations
Cited by 2 Pith papers
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Generalizing conditional average treatment effects from nested randomized trials to all trial-eligible individuals
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Matching-Based Nonparametric Estimation of Group Average Treatment Effects
A matching-and-regression estimator for group average treatment effects is shown consistent, doubly robust, and asymptotically normal, with a bias-corrected version that avoids inverse propensity weighting.
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