mlsanityData to 14 Oct 2026

Causal Generative Neural Networks

arXiv 1711.08936 ↗First posted 24 Nov 2017stat.MLcited by 24

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Abstract (arXiv)

We present Causal Generative Neural Networks (CGNNs) to learn functional causal models from observational data. CGNNs leverage conditional independencies and distributional asymmetries to discover bivariate and multivariate causal structures. CGNNs make no assumption regarding the lack of confounders, and learn a differentiable generative model of the data by using backpropagation. Extensive experiments show their good performances comparatively to the state of the art in observational causal discovery on both simulated and real data, with respect to cause-effect inference, v-structure identification, and multivariate causal discovery.

Key facts. Cited by 24 papers in the map (data through 14 Oct 2026). First posted on arXiv 24 Nov 2017.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 5; 2022: 6; 2023: 4; 2024: 1; 2025: 2; 2026: 6.

24 papers.

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  1. 2021Towards Causal Representation Learningcited by 209
  2. 2022Amortized Inference for Causal Structure Learningcited by 90
  3. 2021Size-Invariant Graph Representations for Graph Classification Extrapolationscited by 84
  4. 2021Efficient Neural Causal Discovery without Acyclicity Constraintscited by 82
  5. 2022Learning to Induce Causal Structurecited by 68
  6. 2022Causal Imitation Learning with Unobserved Confounderscited by 41
  7. 2023Causal Deep Learningcited by 20
  8. 2022Partial Identification of Treatment Effects with Implicit Generative Modelscited by 17
  9. 2023Counterfactual Generative Models for Time-Varying Treatmentscited by 17
  10. 2023Generative Causal Representation Learning for Out-of-Distribution Motion Forecastingcited by 13
  11. 2021Neural Networks for Learning Counterfactual G-Invariances from Single Environmentscited by 8
  12. 2026CauScale: Neural Causal Discovery at Scalecited by 6
12 more
  1. 2022Learning domain-specific causal discovery from time seriescited by 2
  2. 2024Inferring the time-varying coupling of dynamical systems with temporal convolutional autoencoderscited by 2
  3. 2025Causality Is Key to Understand and Balance Multiple Goals in Trustworthy ML and Foundation Modelscited by 2
  4. 2026Topological Residual Asymmetry for Bivariate Causal Directioncited by 2
  5. 2021A Meta Learning Approach to Discerning Causal Graph Structurecited by 1
  6. 2022Causal Structural Hypothesis Testing and Data Generation Modelscited by 1
  7. 2023A Review and Roadmap of Deep Causal Model from Different Causal Structures and Representationscited by 1
  8. 2026Large Causal Models for Temporal Causal Discoverycited by 1
  9. 2026Trustworthy AI Suffers from Invariance Conflicts and Causality is The Solutioncited by 1
  10. 2025CausalRec: A CausalBoost Attention Model for Sequential Recommendationcited by 0
  11. 2026Cohomological Obstructions to Global Counterfactuals: A Sheaf-Theoretic Foundation for Generative Causal Modelscited by 0
  12. 2026Do Real-World Datasets Contain Natural Experiments? An Empirical Study Using Causal Feature Selectioncited by 0