mlsanityData to 7 Oct 2026

A confounding bridge approach for double negative control inference on causal effects

arXiv 1808.04945 ↗First posted 15 Aug 2018stat.MEcited by 36

Not on the map. Why?

Abstract (arXiv)

Unmeasured confounding is a key challenge for causal inference. In this paper, we establish a framework for unmeasured confounding adjustment with negative control variables. A negative control outcome is associated with the confounder but not causally affected by the exposure in view, and a negative control exposure is correlated with the primary exposure or the confounder but does not causally affect the outcome of interest. We introduce an outcome confounding bridge function that depicts the relationship between the confounding effects on the primary outcome and the negative control outcome, and we incorporate a negative control exposure to identify the bridge function and the average causal effect. We also consider the extension to the positive control setting by allowing for nonzero causal effect of the primary exposure on the control outcome. We illustrate our approach with simulations and apply it to a study about the short-term effect of air pollution on mortality. Although a standard analysis shows a significant acute effect of PM2.5 on mortality, our analysis indicates that this effect may be confounded, and after double negative control adjustment, the effect is attenuated toward zero.

Key facts. Cited by 36 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Aug 2018.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 6; 2022: 8; 2023: 8; 2024: 7; 2025: 3; 2026: 4.

36 papers.

Sort:

By area:

  1. 2021Proximal Reinforcement Learning: Efficient Off-Policy Evaluation in Partially Observed Markov Decision Processescited by 40
  2. 2021Deep Proxy Causal Learning and its Application to Confounded Bandit Policy Evaluationcited by 39
  3. 2022Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systemscited by 35
  4. 2022Long-term Causal Inference Under Persistent Confounding via Data Combinationcited by 27
  5. 2022Pessimism in the Face of Confounders: Provably Efficient Offline Reinforcement Learning in Partially Observable Markov Decision Processescited by 26
  6. 2021Proximal Learning for Individualized Treatment Regimes Under Unmeasured Confoundingcited by 21
  7. 2021Minimax Kernel Machine Learning for a Class of Doubly Robust Functionals with Application to Proximal Causal Inferencecited by 20
  8. 2022Future-Dependent Value-Based Off-Policy Evaluation in POMDPscited by 20
  9. 2022Off-Policy Evaluation for Episodic Partially Observable Markov Decision Processes under Non-Parametric Modelscited by 18
  10. 2022Inference on Strongly Identified Functionals of Weakly Identified Functionscited by 12
  11. 2021The Proximal ID Algorithmcited by 11
  12. 2023Debiasing Recommendation by Learning Identifiable Latent Confounderscited by 7
24 more
  1. 2023Partial Identification of Causal Effects Using Proxy Variablescited by 7
  2. 2023A Policy Gradient Method for Confounded POMDPscited by 6
  3. 2024Regularized DeepIV with Model Selectioncited by 6
  4. 2022Optimal Treatment Regimes for Proximal Causal Learningcited by 5
  5. 2021Many Proxy Controlscited by 4
  6. 2023Doubly Robust Proximal Causal Learning for Continuous Treatmentscited by 4
  7. 2024Spillover Detection for Donor Selection in Synthetic Control Modelscited by 4
  8. 2022Blessing from Human-AI Interaction: Super Reinforcement Learning in Confounded Environmentscited by 3
  9. 2024Synthetic Potential Outcomes and Causal Mixture Identifiabilitycited by 3
  10. 2024Causal Inference with Complex Treatments: A Surveycited by 3
  11. 2023A Unified Framework of Policy Learning for Contextual Bandit with Confounding Bias and Missing Observationscited by 2
  12. 2023Assumption-lean falsification tests of rate double-robustness of double-machine-learning estimatorscited by 2
  13. 2024Predictive Performance Comparison of Decision Policies Under Confoundingcited by 2
  14. 2024Automating the Selection of Proxy Variables of Unmeasured Confounderscited by 2
  15. 2023Contagion Effect Estimation Using Proximal Embeddingscited by 1
  16. 2025Learning Exposure Mapping Functions for Inferring Heterogeneous Peer Effectscited by 1
  17. 2023AI in Pharma for Personalized Sequential Decision-Making: Methods, Applications and Opportunitiescited by 0
  18. 2024Assumption-Lean Post-Integrated Inference with Surrogate Control Outcomescited by 0
  19. 2025Sequential Treatment Effect Estimation with Unmeasured Confounderscited by 0
  20. 2025Coupling Generative Modeling and an Autoencoder with the Causal Bridgecited by 0
  21. 2026Adaptive Estimation and Inference in Conditional Moment Models via the Discrepancy Principlecited by 0
  22. 2026Cross-fitted Proximal Learning for Model-Based Reinforcement Learningcited by 0
  23. 2026Decision-Aware Proximal Bridge Learning for Optimal Treatment Selectioncited by 0
  24. 2026Spatiotemporal Proximal Causal Inference under Hidden Confounding and Interferencecited by 0