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Semi-Supervised Treatment Effect Estimation with Unlabeled Covariates for Prediction-Powered Causal Inference

arXiv 2511.08303 ↗First posted 11 Nov 2025stat.ML cs.LG econ.EMcited by 0

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Abstract (arXiv)

This study investigates treatment effect estimation in the semi-supervised setting, also can be interpreted as prediction-powered inference. In our setting, we can use not only the standard triple of covariates, treatment indicator, and outcome, but also unlabeled auxiliary covariates. For this problem, we develop efficiency bounds and efficient estimators whose asymptotic variance aligns with the efficiency bound. In the analysis, we introduce two different data-generating processes: the one-sample setting and the two-sample setting. The one-sample setting considers the case where we can observe treatment indicators and outcomes for a part of the dataset, which is also called the censoring setting. In contrast, the two-sample setting considers two independent datasets with labeled and unlabeled data, which is also called the case-control setting or the stratified setting. In both settings, we find that by incorporating auxiliary covariates, we can lower the efficiency bound and obtain an estimator with an asymptotic variance smaller than that without such auxiliary covariates. We frame our framework as prediction-powered causal inference.

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References · 20

  1. 2021A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantificationcited by 777
  2. 2015Estimation and Inference of Heterogeneous Treatment Effects using Random Forestscited by 597
  3. 2017Nonparametric regression using deep neural networks with ReLU activation functioncited by 431
  4. 2017Positive-Unlabeled Learning with Non-Negative Risk Estimatorcited by 217
  5. 2021Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithmscited by 144
  6. 2020Telescoping Density-Ratio Estimationcited by 108
  7. 2020Non-Negative Bregman Divergence Minimization for Deep Direct Density Ratio Estimationcited by 43
  8. 2021RieszNet and ForestRiesz: Automatic Debiased Machine Learning with Neural Nets and Random Forestscited by 42
  9. 2016Theoretical Comparisons of Positive-Unlabeled Learning against Positive-Negative Learningcited by 41
  10. 2016Covariate Balancing Propensity Score by Tailored Loss Functionscited by 34
  11. 2020Off-Policy Evaluation and Learning for External Validity under a Covariate Shiftcited by 26
  12. 2023Augmented balancing weights as linear regressioncited by 21
8 more
  1. 2024Prediction-powered Generalization of Causal Inferencescited by 14
  2. 2024Active Adaptive Experimental Design for Treatment Effect Estimation with Covariate Choicescited by 12
  3. 2018Efficient nonparametric causal inference with missing exposure informationcited by 10
  4. 2025Direct Bias-Correction Term Estimation for Average Treatment Effect Estimationcited by 8
  5. 2025RieszBoost: Gradient Boosting for Riesz Regressioncited by 6
  6. 2025Nearest Neighbor Matching as Least Squares Density Ratio Estimation and Riesz Regressioncited by 6
  7. 2025Direct Debiased Machine Learning via Bregman Divergence Minimizationcited by 5
  8. 2025A Unified Theory for Causal Inference: Direct Debiased Machine Learning via Bregman-Riesz Regressioncited by 2

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