mlsanityData to 7 Oct 2026

Ancestral Causal Inference

arXiv 1606.07035 ↗First posted 22 Jun 2016cs.LG cs.AI stat.MLcited by 24

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Abstract (arXiv)

Constraint-based causal discovery from limited data is a notoriously difficult challenge due to the many borderline independence test decisions. Several approaches to improve the reliability of the predictions by exploiting redundancy in the independence information have been proposed recently. Though promising, existing approaches can still be greatly improved in terms of accuracy and scalability. We present a novel method that reduces the combinatorial explosion of the search space by using a more coarse-grained representation of causal information, drastically reducing computation time. Additionally, we propose a method to score causal predictions based on their confidence. Crucially, our implementation also allows one to easily combine observational and interventional data and to incorporate various types of available background knowledge. We prove soundness and asymptotic consistency of our method and demonstrate that it can outperform the state-of-the-art on synthetic data, achieving a speedup of several orders of magnitude. We illustrate its practical feasibility by applying it on a challenging protein data set.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Jun 2016.

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Citing papers per year: 2021: 3; 2022: 3; 2023: 3; 2024: 3; 2025: 7; 2026: 5.

24 papers.

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  1. 2023A Survey on Causal Discovery: Theory and Practicecited by 63
  2. 2022BaCaDI: Bayesian Causal Discovery with Unknown Interventionscited by 26
  3. 2024Causal Discovery with Fewer Conditional Independence Testscited by 13
  4. 2022Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventionscited by 12
  5. 2023Local Causal Discovery for Estimating Causal Effectscited by 10
  6. 2025When Selection Meets Intervention: Additional Complexities in Causal Discoverycited by 9
  7. 2023Expert-Aided Causal Discovery of Ancestral Graphscited by 8
  8. 2021A Local Method for Identifying Causal Relations under Markov Equivalencecited by 6
  9. 2022Causal discovery under a confounder blanketcited by 6
  10. 2025Gene Regulatory Network Inference in the Presence of Selection Bias and Latent Confounderscited by 6
  11. 2024Large-Scale Targeted Cause Discovery via Learning from Simulated Datacited by 5
  12. 2026Distributional Equivalence in Linear Non-Gaussian Latent-Variable Cyclic Causal Models: Characterization and Learningcited by 4
12 more
  1. 2025Learning to Defer for Causal Discovery with Imperfect Expertscited by 2
  2. 2025Characterization and Learning of Causal Graphs with Latent Confounders and Post-treatment Selection from Interventional Datacited by 2
  3. 2021Definite Non-Ancestral Relations and Structure Learningcited by 1
  4. 2021Hybrid Bayesian network discovery with latent variables by scoring multiple interventionscited by 1
  5. 2024Comparative Study of Causal Discovery Methods for Cyclic Models with Hidden Confounderscited by 1
  6. 2025dcFCI: Robust Causal Discovery Under Latent Confounding, Unfaithfulness, and Mixed Datacited by 1
  7. 2025Multi-omic Causal Discovery using Genotypes and Gene Expressioncited by 1
  8. 2026On the Number of Conditional Independence Tests in Constraint-based Causal Discoverycited by 1
  9. 2025Causal Bayesian Optimization with Unknown Graphscited by 0
  10. 2026A Topological Sorting Criterion for Random Causal Directed Acyclic Graphscited by 0
  11. 2026Causal ASCEND: Scalable Two-tier Causal Discovery on High Dimensional Multi-omics Datacited by 0
  12. 2026Integrating Background Knowledge for Scalable Causal Discoverycited by 0