mlsanityData to 14 Oct 2026

Structural Agnostic Modeling: Adversarial Learning of Causal Graphs

arXiv 1803.04929 ↗First posted 13 Mar 2018stat.MLcited by 48

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Abstract (arXiv)

A new causal discovery method, Structural Agnostic Modeling (SAM), is presented in this paper. Leveraging both conditional independencies and distributional asymmetries, SAM aims to find the underlying causal structure from observational data. The approach is based on a game between different players estimating each variable distribution conditionally to the others as a neural net, and an adversary aimed at discriminating the generated data against the original data. A learning criterion combining distribution estimation, sparsity and acyclicity constraints is used to enforce the optimization of the graph structure and parameters through stochastic gradient descent. SAM is extensively experimentally validated on synthetic and real data.

Key facts. Cited by 48 papers in the map (data through 14 Oct 2026). First posted on arXiv 13 Mar 2018.

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Citing papers per year: 2021: 5; 2022: 11; 2023: 11; 2024: 14; 2025: 3; 2026: 4.

48 papers.

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  1. 2021D'ya like DAGs? A Survey on Structure Learning and Causal Discoverycited by 211
  2. 2021DiBS: Differentiable Bayesian Structure Learningcited by 106
  3. 2022Large-Scale Differentiable Causal Discovery of Factor Graphscited by 47
  4. 2023Structure Learning with Continuous Optimization: A Sober Look and Beyondcited by 36
  5. 2023A Survey on Causal Discovery Methods for I.I.D. and Time Series Datacited by 35
  6. 2021Towards Federated Bayesian Network Structure Learning with Continuous Optimizationcited by 31
  7. 2023Causal Interpretation of Self-Attention in Pre-Trained Transformerscited by 30
  8. 2024ALCM: Autonomous LLM-Augmented Causal Discovery Frameworkcited by 29
  9. 2022Differentiable Causal Discovery Under Latent Interventionscited by 21
  10. 2023NODAGS-Flow: Nonlinear Cyclic Causal Structure Learningcited by 20
  11. 2024ChatDiet: Empowering Personalized Nutrition-Oriented Food Recommender Chatbots through an LLM-Augmented Frameworkcited by 20
  12. 2024Identifiable Exchangeable Mechanisms for Causal Structure and Representation Learningcited by 16
36 more
  1. 2023Computably Continuous Reinforcement-Learning Objectives are PAC-learnablecited by 13
  2. 2024RealTCD: Temporal Causal Discovery from Interventional Data with Large Language Modelcited by 13
  3. 2024Recursive Causal Discoverycited by 12
  4. 2022GFlowCausal: Generative Flow Networks for Causal Discoverycited by 11
  5. 2023Emerging Synergies in Causality and Deep Generative Models: A Surveycited by 10
  6. 2024Markov Equivalence and Consistency in Differentiable Structure Learningcited by 7
  7. 2022From graphs to DAGs: a low-complexity model and a scalable algorithmcited by 5
  8. 2023Boosting Differentiable Causal Discovery via Adaptive Sample Reweightingcited by 5
  9. 2023Learning Causal Graphs via Monotone Triangular Transport Mapscited by 4
  10. 2024On Discovery of Local Independence over Continuous Variables via Neural Contextual Decompositioncited by 4
  11. 2024psiDAG: Projected Stochastic Approximation Iteration for DAG Structure Learningcited by 4
  12. 2025The Robustness of Differentiable Causal Discovery in Misspecified Scenarioscited by 4
  13. 2022Do learned representations respect causal relationships?cited by 3
  14. 2022Causal Effect Identification in Uncertain Causal Networkscited by 3
  15. 2022On the Sparse DAG Structure Learning Based on Adaptive Lassocited by 3
  16. 2021Scalable Causal Structure Learning: Scoping Review of Traditional and Deep Learning Algorithms and New Opportunities in Biomedicinecited by 2
  17. 2022Trying to Outrun Causality with Machine Learning: Limitations of Model Explainability Techniques for Identifying Predictive Variablescited by 2
  18. 2022A Causal Research Pipeline and Tutorial for Psychologists and Social Scientistscited by 2
  19. 2022A Structure-guided Effective and Temporal-lag Connectivity Network for Revealing Brain Disorder Mechanismscited by 2
  20. 2023A Guide for Practical Use of ADMG Causal Data Augmentationcited by 2
  21. 2021Application of quantum computing to a linear non-Gaussian acyclic model for novel medical knowledge discoverycited by 1
  22. 2023Towards Human-like Perception: Learning Structural Causal Model in Heterogeneous Graphcited by 1
  23. 2024Towards Automated Causal Discovery: a case study on 5G telecommunication datacited by 1
  24. 2024Revisiting Differentiable Structure Learning: Inconsistency of ℓ1 Penalty and Beyondcited by 1
  25. 2024QWO: Speeding Up Permutation-Based Causal Discovery in LiGAMscited by 1
  26. 2026Large Causal Models for Temporal Causal Discoverycited by 1
  27. 2022Reinforcement Causal Structure Learning on Order Graphcited by 0
  28. 2023Causal Structure Learning by Using Intersection of Markov Blanketscited by 0
  29. 2024Boosting Causal Additive Modelscited by 0
  30. 2024Graph Structure Inference with BAM: Introducing the Bilinear Attention Mechanismcited by 0
  31. 2024Graph-attention-based Casual Discovery with Trust Region-navigated Clipping Policy Optimizationcited by 0
  32. 2025Cause-effect perception in an object place taskcited by 0
  33. 2025GRID: Graph-based Reasoning for Intervention and Discovery in Built Environmentscited by 0
  34. 2026Causal Graph Learning via Distributional Invariance of Cause-Effect Relationshipcited by 0
  35. 2026Smoothing the Landscape: Causal Structure Learning via Diffusion Denoising Objectivescited by 0
  36. 2026polyDAG: Polynomial Acyclicity Constraints for Efficient Continuous Causal Discovery in Visual Semantic Graphscited by 0