mlsanityData to 7 Oct 2026

Causal Graph Learning via Distributional Invariance of Cause-Effect Relationship

arXiv 2602.03353 ↗First posted 3 Feb 2026cs.LG cs.AIcited by 0

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Abstract (arXiv)

This paper introduces a new framework for recovering causal graphs from observational data, leveraging the observation that the distribution of an effect, conditioned on its causes, remains invariant to changes in the prior distribution of those causes. This insight enables a direct test for potential causal relationships by checking the variance of their corresponding effect-cause conditional distributions across multiple downsampled subsets of the data. These subsets are selected to reflect different prior cause distributions, while preserving the effect-cause conditional relationships. Using this invariance test and exploiting an (empirical) sparsity of most causal graphs, we develop an algorithm that efficiently uncovers causal relationships with quadratic complexity in the number of observational variables, reducing the processing time by up to 25x compared to state-of-the-art methods. Our empirical experiments on a varied benchmark of large-scale datasets show superior or equivalent performance compared to existing works, while achieving enhanced scalability.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 18 of its 37 references that are in the map. First posted on arXiv 3 Feb 2026.

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References · 18

  1. 2018DAGs with NO TEARS: Continuous Optimization for Structure Learningcited by 745
  2. 2015Causal inference using invariant prediction: identification and confidence intervalscited by 533
  3. 2019Gradient-Based Neural DAG Learningcited by 227
  4. 2019Learning Sparse Nonparametric DAGscited by 207
  5. 2013Learning Bayesian Networks: The Combination of Knowledge and Statistical Datacited by 185
  6. 2013Causal Inference in the Presence of Latent Variables and Selection Biascited by 165
  7. 2021Beware of the Simulated DAG! Causal Discovery Benchmarks May Be Easy To Gamecited by 159
  8. 2017Invariant Causal Prediction for Nonlinear Modelscited by 140
  9. 2022Score matching enables causal discovery of nonlinear additive noise modelscited by 108
  10. 2023Causal-learn: Causal Discovery in Pythoncited by 108
  11. 2019Masked Gradient-Based Causal Structure Learningcited by 89
  12. 2023A Survey on Causal Discovery: Theory and Practicecited by 63
6 more
  1. 2019A Graph Autoencoder Approach to Causal Structure Learningcited by 55
  2. 2018Structural Agnostic Modeling: Adversarial Learning of Causal Graphscited by 48
  3. 2022Causal de Finetti: On the Identification of Invariant Causal Structure in Exchangeable Datacited by 42
  4. 2023Scalable Causal Discovery with Score Matchingcited by 42
  5. 2023A Scale-Invariant Sorting Criterion to Find a Causal Order in Additive Noise Modelscited by 28
  6. 2014The Grow-Shrink strategy for learning Markov network structures constrained by context-specific independencescited by 4

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