Develops DML-PPCI estimators (EE and TMLE variants) that attain the semiparametric efficiency bound derived for semi-supervised causal inference via semi-supervised generalized Riesz regression.
Puate: Semiparametric efficient average treatment effect estimation from treated (positive) and unlabeled units
2 Pith papers cite this work. Polarity classification is still indexing.
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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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Prediction-Powered Causal Inference by Automatic Debiased Machine Learning and Semi-Supervised Riesz Regression
Develops DML-PPCI estimators (EE and TMLE variants) that attain the semiparametric efficiency bound derived for semi-supervised causal inference via semi-supervised generalized Riesz regression.
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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.