mlsanityData to 7 Oct 2026

Differentiable Causal Discovery from Interventional Data

arXiv 2007.01754 ↗First posted 3 Jul 2020cs.LG stat.MLcited by 218

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Abstract (arXiv)

Learning a causal directed acyclic graph from data is a challenging task that involves solving a combinatorial problem for which the solution is not always identifiable. A new line of work reformulates this problem as a continuous constrained optimization one, which is solved via the augmented Lagrangian method. However, most methods based on this idea do not make use of interventional data, which can significantly alleviate identifiability issues. This work constitutes a new step in this direction by proposing a theoretically-grounded method based on neural networks that can leverage interventional data. We illustrate the flexibility of the continuous-constrained framework by taking advantage of expressive neural architectures such as normalizing flows. We show that our approach compares favorably to the state of the art in a variety of settings, including perfect and imperfect interventions for which the targeted nodes may even be unknown.

Key facts. Cited by 218 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Jul 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 17; 2022: 35; 2023: 25; 2024: 50; 2025: 47; 2026: 44.

218 papers.

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  1. 2021Beware of the Simulated DAG! Causal Discovery Benchmarks May Be Easy To Gamecited by 159
  2. 2021Disentanglement via Mechanism Sparsity Regularization: A New Principle for Nonlinear ICAcited by 158
  3. 2022Bayesian Structure Learning with Generative Flow Networkscited by 153
  4. 2022Weakly supervised causal representation learningcited by 140
  5. 2022Interventional Causal Representation Learningcited by 140
  6. 2021DiBS: Differentiable Bayesian Structure Learningcited by 105
  7. 2023Nonparametric Identifiability of Causal Representations from Unknown Interventionscited by 105
  8. 2021The Causal-Neural Connection: Expressiveness, Learnability, and Inferencecited by 97
  9. 2022Amortized Inference for Causal Structure Learningcited by 89
  10. 2021Efficient Neural Causal Discovery without Acyclicity Constraintscited by 82
  11. 2022Learning to Induce Causal Structurecited by 68
  12. 2023GFlowNets for AI-Driven Scientific Discoverycited by 63
38 more
  1. 2023A Survey on Causal Discovery: Theory and Practicecited by 63
  2. 2023Causal Discovery with Language Models as Imperfect Expertscited by 62
  3. 2021gCastle: A Python Toolbox for Causal Discoverycited by 60
  4. 2022Causal Discovery in Heterogeneous Environments Under the Sparse Mechanism Shift Hypothesiscited by 54
  5. 2022Causal Representation Learning for Instantaneous and Temporal Effects in Interactive Systemscited by 50
  6. 2022Interventions, Where and How? Experimental Design for Causal Models at Scalecited by 49
  7. 2022Large-Scale Differentiable Causal Discovery of Factor Graphscited by 47
  8. 2022Differentiable DAG Samplingcited by 46
  9. 2022Causal Structure Learning: a Combinatorial Perspectivecited by 46
  10. 2021Causal Inference for Time series Analysis: Problems, Methods and Evaluationcited by 42
  11. 2021Learning Neural Causal Models with Active Interventionscited by 40
  12. 2023BISCUIT: Causal Representation Learning from Binary Interactionscited by 38
  13. 2022Active Bayesian Causal Inferencecited by 34
  14. 2023Structure Learning with Continuous Optimization: A Sober Look and Beyondcited by 34
  15. 2022Generalizing Goal-Conditioned Reinforcement Learning with Variational Causal Reasoningcited by 33
  16. 2022Learning Causal Representations of Single Cells via Sparse Mechanism Shift Modelingcited by 33
  17. 2023Discovering Causal Relations and Equations from Datacited by 33
  18. 2021Towards Federated Bayesian Network Structure Learning with Continuous Optimizationcited by 31
  19. 2022Bayesian learning of Causal Structure and Mechanisms with GFlowNets and Variational Bayescited by 30
  20. 2023Stable Differentiable Causal Discoverycited by 30
  21. 2022CausalBench: A Large-scale Benchmark for Network Inference from Single-cell Perturbation Datacited by 29
  22. 2022BaCaDI: Bayesian Causal Discovery with Unknown Interventionscited by 26
  23. 2021Interventional Sum-Product Networks: Causal Inference with Tractable Probabilistic Modelscited by 22
  24. 2023Causal Reinforcement Learning: A Surveycited by 22
  25. 2024Causal Composition Diffusion Model for Closed-loop Traffic Generationcited by 22
  26. 2022Differentiable Causal Discovery Under Latent Interventionscited by 21
  27. 2022MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Modelscited by 21
  28. 2022Normalizing Flows for Interventional Density Estimationcited by 21
  29. 2022Deep Learning of Causal Structures in High Dimensionscited by 20
  30. 2023NODAGS-Flow: Nonlinear Cyclic Causal Structure Learningcited by 20
  31. 2023Causal Modeling with Stationary Diffusionscited by 19
  32. 2024Sample, estimate, aggregate: A recipe for causal discovery foundation modelscited by 19
  33. 2024Signature Kernel Conditional Independence Tests in Causal Discovery for Stochastic Processescited by 18
  34. 2021FedDAG: Federated DAG Structure Learningcited by 17
  35. 2021Optimization-based Causal Estimation from Heterogenous Environmentscited by 16
  36. 2022Variational Causal Dynamics: Discovering Modular World Models from Interventionscited by 16
  37. 2022Towards efficient representation identification in supervised learningcited by 14
  38. 2023Structured Neural Networks for Density Estimation and Causal Inferencecited by 13

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