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.