mlsanityData to 7 Oct 2026

Semiparametric proximal causal inference

arXiv 2011.08411 ↗First posted 17 Nov 2020stat.ME math.ST stat.THcited by 58

Not on the map. Why?

Abstract (arXiv)

Skepticism about the assumption of no unmeasured confounding, also known as exchangeability, is often warranted in making causal inferences from observational data; because exchangeability hinges on an investigator's ability to accurately measure covariates that capture all potential sources of confounding. In practice, the most one can hope for is that covariate measurements are at best proxies of the true underlying confounding mechanism operating in a given observational study. In this paper, we consider the framework of proximal causal inference introduced by Miao et al. (2018); Tchetgen Tchetgen et al. (2020), which while explicitly acknowledging covariate measurements as imperfect proxies of confounding mechanisms, offers an opportunity to learn about causal effects in settings where exchangeability on the basis of measured covariates fails. We make a number of contributions to proximal inference including (i) an alternative set of conditions for nonparametric proximal identification of the average treatment effect; (ii) general semiparametric theory for proximal estimation of the average treatment effect including efficiency bounds for key semiparametric models of interest; (iii) a characterization of proximal doubly robust and locally efficient estimators of the average treatment effect. Moreover, we provide analogous identification and efficiency results for the average treatment effect on the treated. Our approach is illustrated via simulation studies and a data application on evaluating the effectiveness of right heart catheterization in the intensive care unit of critically ill patients.

Key facts. Cited by 58 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Nov 2020.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 8; 2022: 10; 2023: 11; 2024: 10; 2025: 11; 2026: 8.

58 papers.

Sort:

By area:

  1. 2021Causal Inference Under Unmeasured Confounding With Negative Controls: A Minimax Learning Approachcited by 43
  2. 2021Proximal Reinforcement Learning: Efficient Off-Policy Evaluation in Partially Observed Markov Decision Processescited by 40
  3. 2022Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systemscited by 35
  4. 2021A Minimax Learning Approach to Off-Policy Evaluation in Confounded Partially Observable Markov Decision Processescited by 34
  5. 2024Applied Causal Inference Powered by ML and AIcited by 30
  6. 2022Long-term Causal Inference Under Persistent Confounding via Data Combinationcited by 27
  7. 2022Pessimism in the Face of Confounders: Provably Efficient Offline Reinforcement Learning in Partially Observable Markov Decision Processescited by 26
  8. 2021Proximal Learning for Individualized Treatment Regimes Under Unmeasured Confoundingcited by 21
  9. 2021Minimax Kernel Machine Learning for a Class of Doubly Robust Functionals with Application to Proximal Causal Inferencecited by 20
  10. 2022Future-Dependent Value-Based Off-Policy Evaluation in POMDPscited by 20
  11. 2022An Instrumental Variable Approach to Confounded Off-Policy Evaluationcited by 20
  12. 2022Estimating individual treatment effects under unobserved confounding using binary instrumentscited by 17
38 more
  1. 2022Deep Learning Methods for Proximal Inference via Maximum Moment Restrictioncited by 13
  2. 2023Minimax Instrumental Variable Regression and L2 Convergence Guarantees without Identification or Closednesscited by 13
  3. 2022Inference on Strongly Identified Functionals of Weakly Identified Functionscited by 12
  4. 2021The Proximal ID Algorithmcited by 11
  5. 2025Doubly Robust Alignment for Large Language Modelscited by 11
  6. 2022Partial Identification with Noisy Covariates: A Robust Optimization Approachcited by 10
  7. 2024Simplifying debiased inference via automatic differentiation and probabilistic programmingcited by 10
  8. 2023Source Condition Double Robust Inference on Functionals of Inverse Problemscited by 8
  9. 2025DeCaFlow: A deconfounding causal generative modelcited by 8
  10. 2023Proximal Causal Learning of Conditional Average Treatment Effectscited by 7
  11. 2023Partial Identification of Causal Effects Using Proxy Variablescited by 7
  12. 2021Causal Inference with Hidden Mediatorscited by 6
  13. 2024Regularized DeepIV with Model Selectioncited by 6
  14. 2024Proxy Methods for Domain Adaptationcited by 6
  15. 2022Optimal Treatment Regimes for Proximal Causal Learningcited by 5
  16. 2021Many Proxy Controlscited by 4
  17. 2023Doubly Robust Proximal Causal Learning for Continuous Treatmentscited by 4
  18. 2024Stochastic Optimization Algorithms for Instrumental Variable Regression with Streaming Datacited by 4
  19. 2025Reinforcement Learning with Continuous Actions Under Unmeasured Confoundingcited by 3
  20. 2023A Unified Framework of Policy Learning for Contextual Bandit with Confounding Bias and Missing Observationscited by 2
  21. 2023Assumption-lean falsification tests of rate double-robustness of double-machine-learning estimatorscited by 2
  22. 2025Causal Effect Identification in lvLiNGAM from Higher-Order Cumulantscited by 2
  23. 2023The Blessings of Multiple Treatments and Outcomes in Treatment Effect Estimationcited by 1
  24. 2024Controlling for Unobserved Confounding with Large Language Model Classification of Patient Smoking Statuscited by 1
  25. 2025Detecting clinician implicit biases in diagnoses using proximal causal inferencecited by 1
  26. 2025Density Ratio-Free Doubly Robust Proxy Causal Learningcited by 1
  27. 2025Comparing Two Proxy Methods for Causal Identificationcited by 1
  28. 2026Proximal Path-Specific Inferencecited by 1
  29. 2023Balanced Filtering via Disclosure-Controlled Proxiescited by 0
  30. 2023A Semiparametric Instrumented Difference-in-Differences Approach to Policy Learningcited by 0
  31. 2023AI in Pharma for Personalized Sequential Decision-Making: Methods, Applications and Opportunitiescited by 0
  32. 2024Recovering Latent Confounders from High-dimensional Proxy Variablescited by 0
  33. 2024REVEAL-IT: REinforcement learning with Visibility of Evolving Agent poLicy for InTerpretabilitycited by 0
  34. 2024Identifiable Latent Bandits: Leveraging observational data for personalized decision-makingcited by 0
  35. 2024DNA-SE: Towards Deep Neural-Nets Assisted Semiparametric Estimationcited by 0
  36. 2025Learning Treatment Representations for Downstream Instrumental Variable Regressioncited by 0
  37. 2025Coupling Generative Modeling and an Autoencoder with the Causal Bridgecited by 0
  38. 2025Transferring Causal Effects using Proxiescited by 0

50 most cited of 58 All 58 (JSON)