mlsanityData to 14 Oct 2026

Typing assumptions improve identification in causal discovery

arXiv 2107.10703 ↗First posted 22 Jul 2021cs.LG cs.AI stat.MLcited by 10

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Abstract (arXiv)

Causal discovery from observational data is a challenging task that can only be solved up to a set of equivalent solutions, called an equivalence class. Such classes, which are often large in size, encode uncertainties about the orientation of some edges in the causal graph. In this work, we propose a new set of assumptions that constrain possible causal relationships based on the nature of variables, thus circumscribing the equivalence class. Namely, we introduce typed directed acyclic graphs, in which variable types are used to determine the validity of causal relationships. We demonstrate, both theoretically and empirically, that the proposed assumptions can result in significant gains in the identification of the causal graph. We also propose causal discovery algorithms that make use of these assumptions and demonstrate their benefits on simulated and pseudo-real data.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 13 of its 46 references that are in the map. First posted on arXiv 22 Jul 2021.

Builds on

13 of 46 references.

References · 13

  1. 2018DAGs with NO TEARS: Continuous Optimization for Structure Learningcited by 752
  2. 2019Causal Confusion in Imitation Learningcited by 288
  3. 2013Causal Discovery with Continuous Additive Noise Modelscited by 255
  4. 2019Causality for Machine Learningcited by 243
  5. 2020Differentiable Causal Discovery from Interventional Datacited by 218
  6. 2013CAM: Causal additive models, high-dimensional order search and penalized regressioncited by 178
  7. 2013Causal Inference and Causal Explanation with Background Knowledgecited by 170
  8. 2017Causal Structure Learningcited by 84
  9. 2018Fast Conditional Independence Test for Vector Variables with Large Sample Sizescited by 16
  10. 2013On the Equivalence of Causal Modelscited by 14
  11. 2015Learning Structures of Bayesian Networks for Variable Groupscited by 12
  12. 2016Causal Discovery for Manufacturing Domainscited by 4
1 more
  1. 2016Formulas for Counting the Sizes of Markov Equivalence Classes of Directed Acyclic Graphscited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 3; 2024: 5; 2025: 1; 2026: 1.

10 papers.

  1. 2023Causal Discovery with Language Models as Imperfect Expertscited by 64
  2. 2024Large Language Models are Effective Priors for Causal Graph Discoverycited by 24
  3. 2024Demystifying amortized causal discovery with transformerscited by 11
  4. 2023Expert-Aided Causal Discovery of Ancestral Graphscited by 8
  5. 2023Causal discovery using dynamically requested knowledgecited by 5
  6. 2024Causal Representation Learning in Temporal Data via Single-Parent Decodingcited by 5
  7. 2024Weakly-supervised causal discovery based on fuzzy knowledge and complex data complementaritycited by 2
  8. 2024Systems with Switching Causal Relations: A Meta-Causal Perspectivecited by 2
  9. 2025Cluster-Dags as Powerful Background Knowledge For Causal Discoverycited by 2
  10. 2026Causal Preference Elicitationcited by 0