mlsanityData to 7 Oct 2026

Causal Inference in the Presence of Latent Variables and Selection Bias

arXiv 1302.4983 ↗First posted 20 Feb 2013cs.AIcited by 165

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Abstract (arXiv)

We show that there is a general, informative and reliable procedure for discovering causal relations when, for all the investigator knows, both latent variables and selection bias may be at work. Given information about conditional independence and dependence relations between measured variables, even when latent variables and selection bias may be present, there are sufficient conditions for reliably concluding that there is a causal path from one variable to another, and sufficient conditions for reliably concluding when no such causal path exists.

Key facts. Cited by 165 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Feb 2013.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 11; 2022: 22; 2023: 25; 2024: 33; 2025: 40; 2026: 34.

165 papers.

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  1. 2021D'ya like DAGs? A Survey on Structure Learning and Causal Discoverycited by 209
  2. 2023Causal-learn: Causal Discovery in Pythoncited by 108
  3. 2021Learning Temporally Causal Latent Processes from General Temporal Datacited by 96
  4. 2021A survey of Bayesian Network structure learningcited by 85
  5. 2023A Survey on Causal Discovery: Theory and Practicecited by 63
  6. 2023Causal Discovery with Language Models as Imperfect Expertscited by 62
  7. 2021Learning latent causal graphs via mixture oraclescited by 55
  8. 2022Causal Discovery in Heterogeneous Environments Under the Sparse Mechanism Shift Hypothesiscited by 54
  9. 2022Causal Structure Learning: a Combinatorial Perspectivecited by 46
  10. 2023A Survey on Causal Reinforcement Learningcited by 43
  11. 2022Causal de Finetti: On the Identification of Invariant Causal Structure in Exchangeable Datacited by 42
  12. 2023Discovering Causal Relations and Equations from Datacited by 33
38 more
  1. 2023A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variablescited by 33
  2. 2024Causal Inference with Large Language Model: A Surveycited by 31
  3. 2024Multi-Agent Causal Discovery Using Large Language Modelscited by 26
  4. 2022Truncated Matrix Power Iteration for Differentiable DAG Learningcited by 23
  5. 2023Self-Compatibility: Evaluating Causal Discovery without Ground Truthcited by 22
  6. 2021Iterative Causal Discovery in the Possible Presence of Latent Confounders and Selection Biascited by 21
  7. 2022Differentiable Causal Discovery Under Latent Interventionscited by 21
  8. 2024Prompting Fairness: Integrating Causality to Debias Large Language Modelscited by 20
  9. 2022Reliable Causal Discovery with Improved Exact Search and Weaker Assumptionscited by 19
  10. 2024Sample, estimate, aggregate: A recipe for causal discovery foundation modelscited by 19
  11. 2021A Theoretical Analysis on Independence-driven Importance Weighting for Covariate-shift Generalizationcited by 18
  12. 2022What-is and How-to for Fairness in Machine Learning: A Survey, Reflection, and Perspectivecited by 18
  13. 2022CausPref: Causal Preference Learning for Out-of-Distribution Recommendationcited by 17
  14. 2022Causal Discovery for Fairnesscited by 16
  15. 2024Identifiable Exchangeable Mechanisms for Causal Structure and Representation Learningcited by 16
  16. 2025Prediction-Powered E-Valuescited by 16
  17. 2022Local Constraint-Based Causal Discovery under Selection Biascited by 15
  18. 2023Causal Discovery with Latent Confounders Based on Higher-Order Cumulantscited by 15
  19. 2021Everything Has a Cause: Leveraging Causal Inference in Legal Text Analysiscited by 14
  20. 2023On Learning Necessary and Sufficient Causal Graphscited by 14
  21. 2024Dynamic Structural Causal Modelscited by 13
  22. 2024Causal Discovery with Fewer Conditional Independence Testscited by 13
  23. 2022Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventionscited by 12
  24. 2024Recursive Causal Discoverycited by 12
  25. 2024The Causal Chambers: Real Physical Systems as a Testbed for AI Methodologycited by 12
  26. 2023Generalized Independent Noise Condition for Estimating Causal Structure with Latent Variablescited by 11
  27. 2023Federated Causality Learning with Explainable Adaptive Optimizationcited by 11
  28. 2021Incorporating Causal Graphical Prior Knowledge into Predictive Modeling via Simple Data Augmentationcited by 10
  29. 2022CIPCaD-Bench: Continuous Industrial Process datasets for benchmarking Causal Discovery methodscited by 10
  30. 2024Causal Agent based on Large Language Modelcited by 10
  31. 2023From Temporal to Contemporaneous Iterative Causal Discovery in the Presence of Latent Confounderscited by 9
  32. 2024Separation-based distance measures for causal graphscited by 9
  33. 2024Detecting and Identifying Selection Structure in Sequential Datacited by 9
  34. 2025When Selection Meets Intervention: Additional Complexities in Causal Discoverycited by 9
  35. 2023Identification of Causal Structure with Latent Variables Based on Higher Order Cumulantscited by 8
  36. 2023Effective Causal Discovery under Identifiable Heteroscedastic Noise Modelcited by 8
  37. 2024Comprehensive Review and Empirical Evaluation of Causal Discovery Algorithms for Numerical Datacited by 8
  38. 2024C2P: Featuring Large Language Models with Causal Reasoningcited by 8

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