mlsanityData to 7 Oct 2026

Scalable and Flexible Causal Discovery with an Efficient Test for Adjacency

arXiv 2406.09177 ↗First posted 13 Jun 2024stat.ML cs.LGcited by 5

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Abstract (arXiv)

To make accurate predictions, understand mechanisms, and design interventions in systems of many variables, we wish to learn causal graphs from large scale data. Unfortunately the space of all possible causal graphs is enormous so scalably and accurately searching for the best fit to the data is a challenge. In principle we could substantially decrease the search space, or learn the graph entirely, by testing the conditional independence of variables. However, deciding if two variables are adjacent in a causal graph may require an exponential number of tests. Here we build a scalable and flexible method to evaluate if two variables are adjacent in a causal graph, the Differentiable Adjacency Test (DAT). DAT replaces an exponential number of tests with a provably equivalent relaxed problem. It then solves this problem by training two neural networks. We build a graph learning method based on DAT, DAT-Graph, that can also learn from data with interventions. DAT-Graph can learn graphs of 1000 variables with state of the art accuracy. Using the graph learned by DAT-Graph, we also build models that make much more accurate predictions of the effects of interventions on large scale RNA sequencing data.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 36 of its 67 references that are in the map. First posted on arXiv 13 Jun 2024.

Builds on

36 of 67 references.

References · 36

  1. 2019Invariant Risk Minimizationcited by 1,832
  2. 2018DAGs with NO TEARS: Continuous Optimization for Structure Learningcited by 745
  3. 2019Gradient-Based Neural DAG Learningcited by 227
  4. 2020Differentiable Causal Discovery from Interventional Datacited by 218
  5. 2021D'ya like DAGs? A Survey on Structure Learning and Causal Discoverycited by 209
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  8. 2021Beware of the Simulated DAG! Causal Discovery Benchmarks May Be Easy To Gamecited by 159
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  11. 2017Approximate Kernel-based Conditional Independence Tests for Fast Non-Parametric Causal Discoverycited by 76
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24 more
  1. 2020DAGs with No Fears: A Closer Look at Continuous Optimization for Learning Bayesian Networkscited by 66
  2. 2022On the Identifiability and Estimation of Causal Location-Scale Noise Modelscited by 56
  3. 2022Large-Scale Differentiable Causal Discovery of Factor Graphscited by 47
  4. 2017Model-Powered Conditional Independence Testcited by 43
  5. 2023Scalable Causal Discovery with Score Matchingcited by 42
  6. 2023Causal Discovery with Score Matching on Additive Models with Arbitrary Noisecited by 41
  7. 2019Conditional Independence Testing using Generative Adversarial Networkscited by 36
  8. 2013A Discovery Algorithm for Directed Cyclic Graphscited by 33
  9. 2020Constraint-Based Causal Discovery using Partial Ancestral Graphs in the presence of Cyclescited by 31
  10. 2023Stable Differentiable Causal Discoverycited by 30
  11. 2020A polynomial-time algorithm for learning nonparametric causal graphscited by 29
  12. 2023Assumption violations in causal discovery and the robustness of score matchingcited by 28
  13. 2020Invariant Representation Learning for Treatment Effect Estimationcited by 25
  14. 2020A Recursive Markov Boundary-Based Approach to Causal Structure Learningcited by 21
  15. 2023NODAGS-Flow: Nonlinear Cyclic Causal Structure Learningcited by 20
  16. 2022Efficient Conditionally Invariant Representation Learningcited by 19
  17. 2023Causal Modeling with Stationary Diffusionscited by 19
  18. 2023Optimizing NOTEARS Objectives via Topological Swapscited by 17
  19. 2020Floodgate: inference for model-free variable importancecited by 15
  20. 2015A Uniformly Consistent Estimator of Causal Effects under the k-Triangle-Faithfulness Assumptioncited by 10
  21. 2020A Weaker Faithfulness Assumption based on Triple Interactionscited by 10
  22. 2019Size of Interventional Markov Equivalence Classes in Random DAG Modelscited by 9
  23. 2023Conditional independence testing under misspecified inductive biasescited by 7
  24. 2023Kernel-based independence tests for causal structure learning on functional datacited by 6

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 3.

5 papers.

  1. 2025CausalMan: A physics-based simulator for large-scale causalitycited by 4
  2. 2026Arrow: A Foundation Model for Causal Discoverycited by 4
  3. 2025Causal Intervention Sequence Analysis for Fault Tracking in Radio Access Networkscited by 3
  4. 2026CausalT5k: Diagnosing Refusal and Failure Modes in Trustworthy Causal Reasoning Across Causal Rungscited by 1
  5. 2026Neural Autoregressive Flows for Markov Boundary Learningcited by 0