mlsanityData to 7 Oct 2026

Deep Learning of Causal Structures in High Dimensions

arXiv 2212.04866 ↗First posted 9 Dec 2022cs.LG cs.AIcited by 20

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Abstract (arXiv)

Recent years have seen rapid progress at the intersection between causality and machine learning. Motivated by scientific applications involving high-dimensional data, in particular in biomedicine, we propose a deep neural architecture for learning causal relationships between variables from a combination of empirical data and prior causal knowledge. We combine convolutional and graph neural networks within a causal risk framework to provide a flexible and scalable approach. Empirical results include linear and nonlinear simulations (where the underlying causal structures are known and can be directly compared against), as well as a real biological example where the models are applied to high-dimensional molecular data and their output compared against entirely unseen validation experiments. These results demonstrate the feasibility of using deep learning approaches to learn causal networks in large-scale problems spanning thousands of variables.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). Builds on 20 of its 56 references that are in the map. First posted on arXiv 9 Dec 2022.

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20 of 56 references.

References · 20

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8 more
  1. 2020Differentiable Causal Discovery from Interventional Datacited by 218
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Citing papers per year: 2023: 1; 2024: 6; 2025: 8; 2026: 5.

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  1. 2024The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applicationscited by 17
  2. 2025An AI-powered Bayesian generative modeling approach for causal inference in observational studiescited by 6
  3. 2024Response Theory via Generative Score Modelingcited by 3
  4. 2025No Foundations without Foundations -- Why semi-mechanistic models are essential for regulatory biologycited by 3
  5. 2023Uncovering wall-shear stress dynamics from neural-network enhanced fluid flow measurementscited by 2
  6. 2024Simulation-based Benchmarking for Causal Structure Learning in Gene Perturbation Experimentscited by 2
  7. 2024Cross-validating causal discovery via Leave-One-Variable-Outcited by 2
  8. 2024Towards Graph Prompt Learning: A Survey and Beyondcited by 1
  9. 2025Causal Graph Neural Networks for Healthcarecited by 1
  10. 2024Causally-Aware Unsupervised Feature Selection Learningcited by 0
  11. 2025Causally-informed Deep Learning towards Explainable and Generalizable Outcomes Prediction in Critical Carecited by 0
  12. 2025TSCAN: Context-Aware Uplift Modeling via Two-Stage Training for Online Merchant Business Diagnosiscited by 0
8 more
  1. 2025DISCO: Mitigating Bias in Deep Learning with Conditional Distance Correlationcited by 0
  2. 2025Temporal Latent Variable Structural Causal Model for Causal Discovery under External Interferencescited by 0
  3. 2025Noise-robust Contrastive Learning for Critical Transition Detection in Dynamical Systemscited by 0
  4. 2026Many Experiments, Few Repetitions, Unpaired Data, and Sparse Effects: Is Causal Inference Possible?cited by 0
  5. 2026Causal Discovery Should Embrace the Wisdom of the Crowdcited by 0
  6. 2026Causal Cellular Context Transfer Learning (C3TL): An Efficient Architecture for Prediction of Unseen Perturbation Effectscited by 0
  7. 2026On the Relationship between Bayesian Networks and Probabilistic Structural Causal Modelscited by 0
  8. 2026Learning Individual Dynamics from Sparse Cross-Sectional Snapshotscited by 0