mlsanityData to 7 Oct 2026

Removing Hidden Confounding by Experimental Grounding

arXiv 1810.11646 ↗First posted 27 Oct 2018stat.ML cs.LGcited by 64

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Abstract (arXiv)

Observational data is increasingly used as a means for making individual-level causal predictions and intervention recommendations. The foremost challenge of causal inference from observational data is hidden confounding, whose presence cannot be tested in data and can invalidate any causal conclusion. Experimental data does not suffer from confounding but is usually limited in both scope and scale. We introduce a novel method of using limited experimental data to correct the hidden confounding in causal effect models trained on larger observational data, even if the observational data does not fully overlap with the experimental data. Our method makes strictly weaker assumptions than existing approaches, and we prove conditions under which it yields a consistent estimator. We demonstrate our method's efficacy using real-world data from a large educational experiment.

Key facts. Cited by 64 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Oct 2018.

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Citing papers per year: 2021: 13; 2022: 6; 2023: 9; 2024: 11; 2025: 17; 2026: 8.

64 papers.

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  1. 2024Causal machine learning for predicting treatment outcomescited by 73
  2. 2021Causal Reinforcement Learning using Observational and Interventional Datacited by 38
  3. 2021A Review of Generalizability and Transportabilitycited by 36
  4. 2022Combining Observational and Randomized Data for Estimating Heterogeneous Treatment Effectscited by 32
  5. 2022Long-term Causal Inference Under Persistent Confounding via Data Combinationcited by 27
  6. 2021Causal Decision Making and Causal Effect Estimation Are Not the Same... and Why It Matterscited by 25
  7. 2022Transfer Learning on Heterogeneous Feature Spaces for Treatment Effects Estimationcited by 21
  8. 2023Balancing Unobserved Confounding with a Few Unbiased Ratings in Debiased Recommendationscited by 18
  9. 2024Prediction-powered Generalization of Causal Inferencescited by 14
  10. 2021Multi-Source Causal Inference Using Control Variatescited by 13
  11. 2022Detecting hidden confounding in observational data using multiple environmentscited by 13
  12. 2024Meta-Learners for Partially-Identified Treatment Effects Across Multiple Environmentscited by 11
38 more
  1. 2022Falsification before Extrapolation in Causal Effect Estimationcited by 10
  2. 2023When does Privileged Information Explain Away Label Noise?cited by 10
  3. 2025Efficient Randomized Experiments Using Foundation Modelscited by 10
  4. 2021Obtaining Causal Information by Merging Datasets with MAXENTcited by 9
  5. 2023Hidden yet quantifiable: A lower bound for confounding strength using randomized trialscited by 8
  6. 2023RCT Rejection Sampling for Causal Estimation Evaluationcited by 7
  7. 2024Adaptive-TMLE for the Average Treatment Effect based on Randomized Controlled Trial Augmented with Real-World Datacited by 7
  8. 2024Combining Experimental and Historical Data for Policy Evaluationcited by 6
  9. 2024Constructing Confidence Intervals for Average Treatment Effects from Multiple Datasetscited by 6
  10. 2022Heterogeneous Treatment Effect with Trained Kernels of the Nadaraya-Watson Regressioncited by 4
  11. 2023Comparison of Methods that Combine Multiple Randomized Trials to Estimate Heterogeneous Treatment Effectscited by 4
  12. 2023Multi-Study R-Learner for Estimating Heterogeneous Treatment Effects Across Studies Using Statistical Machine Learningcited by 4
  13. 2024Causal Inference with Latent Variables: Recent Advances and Future Prospectivescited by 4
  14. 2025The Hardness of Validating Observational Studies with Experimental Datacited by 4
  15. 2025Inverse Reinforcement Learning with Just Classification and a Few Regressionscited by 4
  16. 2023Causal Effect Estimation from Observational and Interventional Data Through Matrix Weighted Linear Estimatorscited by 3
  17. 2021Conditional Cross-Design Synthesis Estimators for Generalizability in Medicaidcited by 2
  18. 2024Estimating Heterogeneous Treatment Effects by Combining Weak Instruments and Observational Datacited by 2
  19. 2025Nonparametric Heterogeneous Long-term Causal Effect Estimation via Data Combinationcited by 2
  20. 2025Long-term Causal Inference via Modeling Sequential Latent Confoundingcited by 2
  21. 2025Causal-EPIG: A Prediction-Oriented Active Learning Framework for CATE Estimationcited by 2
  22. 2021Causal Markov Boundariescited by 1
  23. 2021Synthesized Difference in Differencescited by 1
  24. 2021Generalizing Clinical Trials with Convex Hullscited by 1
  25. 2023Counterfactual Prediction Under Selective Confoundingcited by 1
  26. 2023Estimating treatment effects from single-arm trials via latent-variable modelingcited by 1
  27. 2024Combining Incomplete Observational and Randomized Data for Heterogeneous Treatment Effectscited by 1
  28. 2025Robust estimation of heterogeneous treatment effects in randomized trials leveraging external datacited by 1
  29. 2025Meta-Router: Bridging Gold-standard and Preference-based Evaluations in Large Language Model Routingcited by 1
  30. 2026Detecting Unobserved Confounders: A Kernelized Regression Approachcited by 1
  31. 2026Observationally Informed Adaptive Causal Experimental Designcited by 1
  32. 2026Improving RCT-Based Treatment Effect Estimation Under Covariate Mismatch via Calibrated Alignmentcited by 1
  33. 2021Combining Interventional and Observational Data Using Causal Reductionscited by 0
  34. 2021Quantum causal inference in the presence of hidden common causes: An entropic approachcited by 0
  35. 2021Learning Pareto-Efficient Decisions with Confidencecited by 0
  36. 2021ADCB: An Alzheimer's disease benchmark for evaluating observational estimators of causal effectscited by 0
  37. 2024Benchmarks for Reinforcement Learning with Biased Offline Data and Imperfect Simulatorscited by 0
  38. 2024Accounting for Missing Covariates in Heterogeneous Treatment Estimationcited by 0

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