mlsanityData to 14 Oct 2026

Coarsening Causal DAG Models

arXiv 2601.10531 ↗First posted 15 Jan 2026stat.ML cs.LG math.COcited by 4

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Abstract (arXiv)

Directed acyclic graphical (DAG) models are a powerful tool for representing causal relationships among jointly distributed random variables, especially concerning data from across different experimental settings. However, it is not always practical or desirable to estimate a causal model at the granularity of given features in a particular dataset. There is a growing body of research on causal abstraction to address such problems. We contribute to this line of research by (i) providing novel graphical identifiability results for practically-relevant interventional settings, (ii) proposing an efficient, provably consistent algorithm for directly learning abstract causal graphs from interventional data with unknown intervention targets, and (iii) uncovering theoretical insights about the lattice structure of the underlying search space, with connections to the field of causal discovery more generally. As proof of concept, we apply our algorithm on synthetic and real datasets with known ground truths, including measurements from a controlled physical system with interacting light intensity and polarization.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 34 of its 66 references that are in the map. First posted on arXiv 15 Jan 2026.

Builds on

34 of 66 references.

References · 34

  1. 2017Causal Consistency of Structural Equation Modelscited by 118
  2. 2018Abstracting Causal Modelscited by 107
  3. 2017Permutation-based Causal Inference Algorithms with Interventionscited by 78
  4. 2019Permutation-Based Causal Structure Learning with Unknown Intervention Targetscited by 69
  5. 2022Diffusion Models for Causal Discovery via Topological Orderingcited by 66
  6. 2019Approximate Causal Abstractioncited by 63
  7. 2022Causal Effect Identification in Cluster DAGscited by 36
  8. 2023Learning nonparametric latent causal graphs with unknown interventionscited by 34
  9. 2015Multi-Level Cause-Effect Systemscited by 30
  10. 2023Jointly Learning Consistent Causal Abstractions Over Multiple Interventional Distributionscited by 23
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  12. 2024Neural Causal Abstractionscited by 21
22 more
  1. 2022Causal Structure Discovery between Clusters of Nodes Induced by Latent Factorscited by 18
  2. 2023Targeted Reduction of Causal Modelscited by 15
  3. 2019Measurement Dependence Inducing Latent Causal Modelscited by 14
  4. 2019Learning big Gaussian Bayesian networks: partition, estimation, and fusioncited by 13
  5. 2022On the Equivalence of Causal Models: A Category-Theoretic Approachcited by 13
  6. 2023Causal Optimal Transport of Abstractionscited by 13
  7. 2021Clustering and Structural Robustness in Causal Diagramscited by 12
  8. 2022Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventionscited by 12
  9. 2022Vector causal inference between two groups of variablescited by 11
  10. 2024Learning Causal Abstractions of Linear Structural Causal Modelscited by 11
  11. 2021A Distance Covariance-based Kernel for Nonlinear Causal Clustering in Heterogeneous Populationscited by 10
  12. 2023Do we become wiser with time? On causal equivalence with tiered background knowledgecited by 9
  13. 2025Causal Abstraction Learning based on the Semantic Embedding Principlecited by 9
  14. 2024Aligning Graphical and Functional Causal Abstractionscited by 6
  15. 2025Nonlinear Causal Discovery for Grouped Datacited by 5
  16. 2020Equivalent Causal Modelscited by 4
  17. 2022A Transformational Characterization of Unconditionally Equivalent Bayesian Networkscited by 4
  18. 2022Combinatorial and algebraic perspectives on the marginal independence structure of Bayesian networkscited by 3
  19. 2025Distributionally Robust Causal Abstractionscited by 3
  20. 2024Causal Layering via Conditional Entropycited by 1
  21. 2025Learning Nonlinear Causal Reductions to Explain Reinforcement Learning Policiescited by 1
  22. 2025Using causal abstractions to accelerate decision-making in complex bandit problemscited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 4.

4 papers.

  1. 2026Coarsening Linear Non-Gaussian Causal Models with Cyclescited by 2
  2. 2026Invariance of Clustering Operations in Causal Effect Identificationcited by 0
  3. 2026GO-Based Clustering for Learning Cluster-Level Causal Gene Regulatory Networkscited by 0
  4. 2026Score-Based Learning of Cluster DAGs from Interventionscited by 0