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Modified Causal Forest
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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 estimation and inference procedures for multiple treatment models in a selection-on-observed-variables framework by modifying the Causal Forest approach (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 pro-gramme shows their value for applied research.
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Cited by 1 Pith paper
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A Machine-Learning-Compatible Omnibus Test for Treatment Effect Heterogeneity
A locally robust, cross-fitted omnibus test detects systematic treatment-effect heterogeneity with respect to low-dimensional covariates while allowing high-dimensional ML-based nuisance estimation across unconfounded...
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