mlsanityData to 7 Oct 2026

Supervised Whole DAG Causal Discovery

arXiv 2006.04697 ↗First posted 8 Jun 2020cs.LG cs.AI stat.MLcited by 19

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Abstract (arXiv)

We propose to address the task of causal structure learning from data in a supervised manner. Existing work on learning causal directions by supervised learning is restricted to learning pairwise relation, and not well suited for whole DAG discovery. We propose a novel approach of modeling the whole DAG structure discovery as a supervised learning. To fit the problem in hand, we propose to use permutation equivariant models that align well with the problem domain. We evaluate the proposed approach extensively on synthetic graphs of size 10,20,50,100 and real data, and show promising results compared with a variety of previous approaches.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Jun 2020.

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Citing papers per year: 2021: 1; 2022: 3; 2023: 2; 2024: 7; 2025: 4; 2026: 2.

19 papers.

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  1. 2022Amortized Inference for Causal Structure Learningcited by 89
  2. 2022Learning to Induce Causal Structurecited by 68
  3. 2023Causal Discovery from Temporal Data: An Overview and New Perspectivescited by 43
  4. 2024MECD: Unlocking Multi-Event Causal Discovery in Video Reasoningcited by 23
  5. 2024A Meta-Learning Approach to Bayesian Causal Discoverycited by 22
  6. 2021ML4C: Seeing Causality Through Latent Vicinitycited by 11
  7. 2022Causal discovery for observational sciences using supervised machine learningcited by 10
  8. 2024Demystifying amortized causal discovery with transformerscited by 10
  9. 2024Embracing the black box: Heading towards foundation models for causal discovery from time series datacited by 5
  10. 2024Are you doing better than random guessing? A call for using negative controls when evaluating causal discovery algorithmscited by 5
  11. 2025MECD+: Unlocking Event-Level Causal Graph Discovery for Video Reasoningcited by 5
  12. 2025Learning Causal Graphs at Scale: A Foundation Model Approachcited by 4
7 more
  1. 2023Learned Causal Method Predictioncited by 3
  2. 2026Test-Time Learning of Causal Structure from Interventional Datacited by 2
  3. 2024Identifying biological perturbation targets through causal differential networkscited by 1
  4. 2025ACTIVA: Amortized Causal Effect Estimation via Transformer-based Variational Autoencodercited by 1
  5. 2024Graph Structure Inference with BAM: Introducing the Bilinear Attention Mechanismcited by 0
  6. 2025From Observations to Causations: A GNN-based Probabilistic Prediction Framework for Causal Discoverycited by 0
  7. 2026Demistifying Data and Simulator Assumptions in Supervised Causal Discoverycited by 0