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.
Density ratio matching under the bregman divergence: A unified framework of density ratio estimation
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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.