mlsanityData to 7 Oct 2026

On the role of surrogates in the efficient estimation of treatment effects with limited outcome data

arXiv 2003.12408 ↗First posted 27 Mar 2020stat.ML cs.LG stat.MEcited by 36

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Abstract (arXiv)

In many experimental and observational studies, the outcome of interest is often difficult or expensive to observe, reducing effective sample sizes for estimating average treatment effects (ATEs) even when identifiable. We study how incorporating data on units for which only surrogate outcomes not of primary interest are observed can increase the precision of ATE estimation. We refrain from imposing stringent surrogacy conditions, which permit surrogates as perfect replacements for the target outcome. Instead, we supplement the available, albeit limited, observations of the target outcome with abundant observations of surrogate outcomes, without any assumptions beyond unconfounded treatment assignment and missingness and corresponding overlap conditions. To quantify the potential gains, we derive the difference in efficiency bounds on ATE estimation with and without surrogates, both when an overwhelming or comparable number of units have missing outcomes. We develop robust ATE estimation and inference methods that realize these efficiency gains. We empirically demonstrate the gains by studying long-term-earning effects of job training.

Key facts. Cited by 36 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Mar 2020.

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Citing papers per year: 2021: 3; 2022: 7; 2023: 5; 2024: 7; 2025: 9; 2026: 5.

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  1. 2023Prediction-Powered Inferencecited by 214
  2. 2022Off-Policy Evaluation for Large Action Spaces via Embeddingscited by 53
  3. 2023Using Imperfect Surrogates for Downstream Inference: Design-based Supervised Learning for Social Science Applications of Large Language Modelscited by 37
  4. 2025Predictions as Surrogates: Revisiting Surrogate Outcomes in the Age of AIcited by 31
  5. 2022Long-term Causal Inference Under Persistent Confounding via Data Combinationcited by 27
  6. 2023Assumption-Lean and Data-Adaptive Post-Prediction Inferencecited by 25
  7. 2022Long-term Causal Effects Estimation via Latent Surrogates Representation Learningcited by 11
  8. 2023Choosing a Proxy Metric from Past Experimentscited by 11
  9. 2024Long-term Off-Policy Evaluation and Learningcited by 9
  10. 2024Policy Learning for Balancing Short-Term and Long-Term Rewardscited by 7
  11. 2021Surrogate Assisted Semi-supervised Inference for High Dimensional Risk Predictioncited by 6
  12. 2022A New Central Limit Theorem for the Augmented IPW Estimator: Variance Inflation, Cross-Fit Covariance and Beyondcited by 5
24 more
  1. 2021Double Robust Semi-Supervised Inference for the Mean: Selection Bias under MAR Labeling with Decaying Overlapcited by 4
  2. 2022Kernel methods for long term dose response curvescited by 4
  3. 2022Efficient learning of nonlinear prediction models with time-series privileged informationcited by 4
  4. 2024Estimating Long-term Heterogeneous Dose-response Curve: Generalization Bound Leveraging Optimal Transport Weightscited by 4
  5. 2025Long-Term Individual Causal Effect Estimation via Identifiable Latent Representation Learningcited by 3
  6. 2022Semi-supervised Batch Learning From Logged Datacited by 2
  7. 2024On the Role of Surrogates in Conformal Inference of Individual Causal Effectscited by 2
  8. 2025Nonparametric Heterogeneous Long-term Causal Effect Estimation via Data Combinationcited by 2
  9. 2025Long-term Causal Inference via Modeling Sequential Latent Confoundingcited by 2
  10. 2025Causal-EPIG: A Prediction-Oriented Active Learning Framework for CATE Estimationcited by 2
  11. 2025Causal Judge Evaluation: Calibrated Surrogate Metrics for LLM Systemscited by 2
  12. 2021Calibrated Optimal Decision Making with Multiple Data Sources and Limited Outcomecited by 1
  13. 2023Identification and Estimation for Nonignorable Missing Data: A Data Fusion Approachcited by 1
  14. 2024Continuous Treatment Effects with Surrogate Outcomescited by 1
  15. 2025ActiveCQ: Active Estimation of Causal Quantitiescited by 1
  16. 2026Statistical Foundations of LLM-based A/B Testing: A Surrogacy Framework for Human Causal Inferencecited by 1
  17. 2024Pareto-Optimal Estimation and Policy Learning on Short-term and Long-term Treatment Effectscited by 0
  18. 2024Predicting Long Term Sequential Policy Value Using Softer Surrogatescited by 0
  19. 2025Optimal Policy Adaptation under Covariate Shiftcited by 0
  20. 2025Identification and Estimation of Long-Term Treatment Effects with Monotone Missingcited by 0
  21. 2026Orthogonal Learner for Estimating Heterogeneous Long-Term Treatment Effectscited by 0
  22. 2026Debiased Machine Learning for Conformal Prediction of Counterfactual Outcomes Under Runtime Confoundingcited by 0
  23. 2026Semi-Supervised Conditional Diffusion via Label Augmentationcited by 0
  24. 2026Representation Learning for Sample-Efficient CATE Estimation by Leveraging Multiple Outcomescited by 0