mlsanityData to 14 Oct 2026

Iterative Causal Discovery in the Possible Presence of Latent Confounders and Selection Bias

arXiv 2111.04095 ↗First posted 7 Nov 2021cs.LG cs.AI stat.MEcited by 21

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Abstract (arXiv)

We present a sound and complete algorithm, called iterative causal discovery (ICD), for recovering causal graphs in the presence of latent confounders and selection bias. ICD relies on the causal Markov and faithfulness assumptions and recovers the equivalence class of the underlying causal graph. It starts with a complete graph, and consists of a single iterative stage that gradually refines this graph by identifying conditional independence (CI) between connected nodes. Independence and causal relations entailed after any iteration are correct, rendering ICD anytime. Essentially, we tie the size of the CI conditioning set to its distance on the graph from the tested nodes, and increase this value in the successive iteration. Thus, each iteration refines a graph that was recovered by previous iterations having smaller conditioning sets -- a higher statistical power -- which contributes to stability. We demonstrate empirically that ICD requires significantly fewer CI tests and learns more accurate causal graphs compared to FCI, FCI+, and RFCI algorithms (code is available at https://github.com/IntelLabs/causality-lab).

Key facts. Cited by 21 papers in the map (data through 14 Oct 2026). Builds on 5 of its 20 references that are in the map. First posted on arXiv 7 Nov 2021.

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5 of 20 references.

References · 5

  1. 2013Causal Inference in the Presence of Latent Variables and Selection Biascited by 166
  2. 2013Learning Sparse Causal Models is not NP-hardcited by 49
  3. 2018Causal Identification under Markov Equivalencecited by 15
  4. 2018Bayesian Structure Learning by Recursive Bootstrapcited by 8
  5. 2021Improving Efficiency and Accuracy of Causal Discovery Using a Hierarchical Wrappercited by 6

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Cited by year (2026 to 14 Oct)

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

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  1. 2023Causal Discovery from Temporal Data: An Overview and New Perspectivescited by 43
  2. 2023Causal Interpretation of Self-Attention in Pre-Trained Transformerscited by 30
  3. 2023From Temporal to Contemporaneous Iterative Causal Discovery in the Presence of Latent Confounderscited by 9
  4. 2025Gene Regulatory Network Inference in the Presence of Selection Bias and Latent Confounderscited by 7
  5. 2023A Survey of Methods, Challenges and Perspectives in Causalitycited by 6
  6. 2024Scalable Differentiable Causal Discovery in the Presence of Latent Confounders with Skeleton Posterior (Extended Version)cited by 6
  7. 2024A Causal World Model Underlying Next Token Prediction: Exploring GPT in a Controlled Environmentcited by 6
  8. 2023An Explainable AI Approach to Large Language Model Assisted Causal Model Auditing and Developmentcited by 5
  9. 2025Causal Climate Emulation with Bayesian Filteringcited by 5
  10. 2025Efficient Ensemble Conditional Independence Test Framework for Causal Discoverycited by 5
  11. 2022CLEAR: Causal Explanations from Attention in Neural Recommenderscited by 4
  12. 2025On Different Notions of Redundancy in Conditional-Independence-Based Discovery of Graphical Modelscited by 3
9 more
  1. 2022GT-CausIn: a novel causal-based insight for traffic predictioncited by 2
  2. 2022Interpretability and causal discovery of the machine learning models to predict the production of CBM wells after hydraulic fracturingcited by 2
  3. 2025Characterization and Learning of Causal Graphs with Latent Confounders and Post-treatment Selection from Interventional Datacited by 2
  4. 2025Score-based Greedy Search for Structure Identification of Partially Observed Linear Causal Modelscited by 2
  5. 2023Recovering Linear Causal Models with Latent Variables via Cholesky Factorization of Covariance Matrixcited by 1
  6. 2024Demystifying the Physics of Deep Reinforcement Learning-Based Autonomous Vehicle Decision-Makingcited by 0
  7. 2026Efficient Differentiable Causal Discovery via Reliable Super-Structure Learningcited by 0
  8. 2026A Recursive Decomposition Framework for Causal Structure Learning in the Presence of Latent Variablescited by 0
  9. 2026CausalSteward: An Agentic Divide-Conquer-Combine Copilot for Causal Discoverycited by 0