mlsanityData to 7 Oct 2026

The Proximal ID Algorithm

arXiv 2108.06818 ↗First posted 15 Aug 2021stat.ME cs.AIcited by 11

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Abstract (arXiv)

Unobserved confounding is a fundamental obstacle to establishing valid causal conclusions from observational data. Two complementary types of approaches have been developed to address this obstacle: obtaining identification using fortuitous external aids, such as instrumental variables or proxies, or by means of the ID algorithm, using Markov restrictions on the full data distribution encoded in graphical causal models. In this paper we aim to develop a synthesis of the former and latter approaches to identification in causal inference to yield the most general identification algorithm in multivariate systems currently known -- the proximal ID algorithm. In addition to being able to obtain nonparametric identification in all cases where the ID algorithm succeeds, our approach allows us to systematically exploit proxies to adjust for the presence of unobserved confounders that would have otherwise prevented identification. In addition, we outline a class of estimation strategies for causal parameters identified by our method in an important special case. We illustrate our approach by simulation studies and a data application.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 10 of its 49 references that are in the map. First posted on arXiv 15 Aug 2021.

Builds on

10 of 49 references.

References · 10

  1. 2016Identifying Causal Effects With Proxy Variables of an Unmeasured Confoundercited by 138
  2. 2020An Introduction to Proximal Causal Learningcited by 91
  3. 2020Semiparametric proximal causal inferencecited by 58
  4. 2021Causal Inference Under Unmeasured Confounding With Negative Controls: A Minimax Learning Approachcited by 43
  5. 2017Nested Markov Properties for Acyclic Directed Mixed Graphscited by 38
  6. 2018A confounding bridge approach for double negative control inference on causal effectscited by 36
  7. 2019The Case for Evaluating Causal Models Using Interventional Measures and Empirical Datacited by 30
  8. 2020Kernel Methods for Unobserved Confounding: Negative Controls, Proxies, and Instrumentscited by 27
  9. 2019A Potential Outcomes Calculus for Identifying Conditional Path-Specific Effectscited by 19
  10. 2020Multivariate Counterfactual Systems And Causal Graphical Modelscited by 9

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Cited by year

Citing papers per year: 2021: 1; 2022: 3; 2023: 5; 2024: 2.

11 papers.

  1. 2022Causal Structure Learning: a Combinatorial Perspectivecited by 46
  2. 2022Partial Identification with Noisy Covariates: A Robust Optimization Approachcited by 10
  3. 2023Proximal Causal Learning of Conditional Average Treatment Effectscited by 7
  4. 2023Partial Identification of Causal Effects Using Proxy Variablescited by 7
  5. 2021Causal Inference with Hidden Mediatorscited by 6
  6. 2022Optimal Treatment Regimes for Proximal Causal Learningcited by 5
  7. 2023Non-parametric identifiability and sensitivity analysis of synthetic control modelscited by 5
  8. 2023When does the ID algorithm fail?cited by 4
  9. 2024Spillover Detection for Donor Selection in Synthetic Control Modelscited by 4
  10. 2023Assumption-lean falsification tests of rate double-robustness of double-machine-learning estimatorscited by 2
  11. 2024Automating the Selection of Proxy Variables of Unmeasured Confounderscited by 2