mlsanityData to 7 Oct 2026

Kernel Methods for Unobserved Confounding: Negative Controls, Proxies, and Instruments

arXiv 2012.10315 ↗First posted 18 Dec 2020stat.ML cs.LG econ.EMcited by 27

Not on the map. Why?

Abstract (arXiv)

Negative control is a strategy for learning the causal relationship between treatment and outcome in the presence of unmeasured confounding. The treatment effect can nonetheless be identified if two auxiliary variables are available: a negative control treatment (which has no effect on the actual outcome), and a negative control outcome (which is not affected by the actual treatment). These auxiliary variables can also be viewed as proxies for a traditional set of control variables, and they bear resemblance to instrumental variables. I propose a family of algorithms based on kernel ridge regression for learning nonparametric treatment effects with negative controls. Examples include dose response curves, dose response curves with distribution shift, and heterogeneous treatment effects. Data may be discrete or continuous, and low, high, or infinite dimensional. I prove uniform consistency and provide finite sample rates of convergence. I estimate the dose response curve of cigarette smoking on infant birth weight adjusting for unobserved confounding due to household income, using a data set of singleton births in the state of Pennsylvania between 1989 and 1991.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Dec 2020.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 8; 2022: 6; 2023: 8; 2024: 3; 2025: 1; 2026: 1.

27 papers.

Sort:

By area:

  1. 2021Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restrictioncited by 54
  2. 2021Causal Inference Under Unmeasured Confounding With Negative Controls: A Minimax Learning Approachcited by 43
  3. 2021Deep Proxy Causal Learning and its Application to Confounded Bandit Policy Evaluationcited by 39
  4. 2021A Minimax Learning Approach to Off-Policy Evaluation in Confounded Partially Observable Markov Decision Processescited by 34
  5. 2022Long-term Causal Inference Under Persistent Confounding via Data Combinationcited by 27
  6. 2022Pessimism in the Face of Confounders: Provably Efficient Offline Reinforcement Learning in Partially Observable Markov Decision Processescited by 26
  7. 2021Minimax Kernel Machine Learning for a Class of Doubly Robust Functionals with Application to Proximal Causal Inferencecited by 20
  8. 2022Off-Policy Evaluation for Episodic Partially Observable Markov Decision Processes under Non-Parametric Modelscited by 18
  9. 2022Deep Learning Methods for Proximal Inference via Maximum Moment Restrictioncited by 13
  10. 2022Inference on Strongly Identified Functionals of Weakly Identified Functionscited by 12
  11. 2021The Proximal ID Algorithmcited by 11
  12. 2023Proximal Causal Learning of Conditional Average Treatment Effectscited by 7
15 more
  1. 2023Kernel-based off-policy estimation without overlap: Instance optimality beyond semiparametric efficiencycited by 6
  2. 2024Regularized DeepIV with Model Selectioncited by 6
  3. 2022Optimal Treatment Regimes for Proximal Causal Learningcited by 5
  4. 2023Kernel Single Proxy Control for Deterministic Confoundingcited by 5
  5. 2021Many Proxy Controlscited by 4
  6. 2023Doubly Robust Proximal Causal Learning for Continuous Treatmentscited by 4
  7. 2024Causal Inference with Complex Treatments: A Surveycited by 3
  8. 2023Kernel Conditional Moment Constraints for Confounding Robust Inferencecited by 2
  9. 2023A Unified Framework of Policy Learning for Contextual Bandit with Confounding Bias and Missing Observationscited by 2
  10. 2024Automating the Selection of Proxy Variables of Unmeasured Confounderscited by 2
  11. 2021Generalized Kernel Ridge Regression for Causal Inference with Missing-at-Random Sample Selectioncited by 1
  12. 2023Contagion Effect Estimation Using Proximal Embeddingscited by 1
  13. 2025Density Ratio-Free Doubly Robust Proxy Causal Learningcited by 1
  14. 2023A Convex Framework for Confounding Robust Inferencecited by 0
  15. 2026Spatiotemporal Proximal Causal Inference under Hidden Confounding and Interferencecited by 0