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 unconfoundedness, DiD, and IV designs.
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ECO-ATE is a federated semiparametrically efficient estimator for the average treatment effect on a target population that incorporates summary statistics from source populations while allowing distributional shifts.
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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 unconfoundedness, DiD, and IV designs.
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Efficient collaborative learning of the average treatment effect
ECO-ATE is a federated semiparametrically efficient estimator for the average treatment effect on a target population that incorporates summary statistics from source populations while allowing distributional shifts.