Incorporating unlabeled auxiliary covariates lowers the efficiency bound for treatment effect estimation and produces estimators with smaller asymptotic variance than those without the auxiliary data.
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Balancing in debiased machine learning for causal effects should be guided by the Neyman orthogonal score, with covariate balancing as a special case appropriate only when regression errors depend solely on covariates.
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Semi-Supervised Treatment Effect Estimation with Unlabeled Covariates for Prediction-Powered Causal Inference
Incorporating unlabeled auxiliary covariates lowers the efficiency bound for treatment effect estimation and produces estimators with smaller asymptotic variance than those without the auxiliary data.
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Covariate Balancing and Riesz Regression Should Be Guided by the Neyman Orthogonal Score in Debiased Machine Learning
Balancing in debiased machine learning for causal effects should be guided by the Neyman orthogonal score, with covariate balancing as a special case appropriate only when regression errors depend solely on covariates.