mlsanityData to 14 Oct 2026

Causal Discovery on Higher-Order Interactions

arXiv 2511.14206 ↗First posted 18 Nov 2025stat.ML cs.LGcited by 2

Not on the map. Why?

Abstract (arXiv)

Causal discovery combines data with knowledge provided by experts to learn the DAG representing the causal relationships between a given set of variables. When data are scarce, bagging is used to measure our confidence in an average DAG obtained by aggregating bootstrapped DAGs. However, the aggregation step has received little attention from the specialized literature: the average DAG is constructed using only the confidence in the individual edges of the bootstrapped DAGs, thus disregarding complex higher-order edge structures. In this paper, we introduce a novel theoretical framework based on higher-order structures and describe a new DAG aggregation algorithm. We perform a simulation study, discussing the advantages and limitations of the proposed approach. Our proposal is both computationally efficient and effective, outperforming state-of-the-art solutions, especially in low sample size regimes and under high dimensionality settings.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 14 of its 48 references that are in the map. First posted on arXiv 18 Nov 2025.

Builds on

14 of 48 references.

References · 14

  1. 2021D'ya like DAGs? A Survey on Structure Learning and Causal Discoverycited by 211
  2. 2013Learning Bayesian Networks: The Combination of Knowledge and Statistical Datacited by 187
  3. 2016Foundations of Structural Causal Models with Cycles and Latent Variablescited by 134
  4. 2023A Survey on Causal Discovery: Theory and Practicecited by 63
  5. 2014Constraint-based Causal Discovery from Multiple Interventions over Overlapping Variable Setscited by 48
  6. 2018Causal Discovery in the Presence of Missing Datacited by 47
  7. 2015High-dimensional consistency in score-based and hybrid structure learningcited by 46
  8. 2021The impact of prior knowledge on causal structure learningcited by 35
  9. 2013Data Analysis with Bayesian Networks: A Bootstrap Approachcited by 32
  10. 2013Being Bayesian about Network Structurecited by 17
  11. 2021Greedy structure learning from data that contain systematic missing valuescited by 13
  12. 2018Bayesian Structure Learning by Recursive Bootstrapcited by 8
2 more
  1. 2023The Impact of Missing Data on Causal Discovery: A Multicentric Clinical Studycited by 3
  2. 2023Risk Assessment of Lymph Node Metastases in Endometrial Cancer Patients: A Causal Approachcited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Stable Causal Discovery via Directed Acyclic Graph Aggregationcited by 0
  2. 2026Dynamic Structural Causal Modeling for Sleepcited by 0