mlsanityData to 7 Oct 2026

Do Not Marginalize Mechanisms, Rather Consolidate!

arXiv 2310.08377 ↗First posted 12 Oct 2023cs.AIcited by 1

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Abstract (arXiv)

Structural causal models (SCMs) are a powerful tool for understanding the complex causal relationships that underlie many real-world systems. As these systems grow in size, the number of variables and complexity of interactions between them does, too. Thus, becoming convoluted and difficult to analyze. This is particularly true in the context of machine learning and artificial intelligence, where an ever increasing amount of data demands for new methods to simplify and compress large scale SCM. While methods for marginalizing and abstracting SCM already exist today, they may destroy the causality of the marginalized model. To alleviate this, we introduce the concept of consolidating causal mechanisms to transform large-scale SCM while preserving consistent interventional behaviour. We show consolidation is a powerful method for simplifying SCM, discuss reduction of computational complexity and give a perspective on generalizing abilities of consolidated SCM.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 14 of its 30 references that are in the map. First posted on arXiv 12 Oct 2023.

Builds on

14 of 30 references.

References · 14

  1. 2022Weakly supervised causal representation learningcited by 140
  2. 2016Foundations of Structural Causal Models with Cycles and Latent Variablescited by 134
  3. 2017Causal Consistency of Structural Equation Modelscited by 114
  4. 2018Abstracting Causal Modelscited by 104
  5. 2019Approximate Causal Abstractioncited by 62
  6. 2020Causal models for dynamical systemscited by 34
  7. 2015Multi-Level Cause-Effect Systemscited by 30
  8. 2018Causal Modeling of Dynamical Systemscited by 24
  9. 2023Jointly Learning Consistent Causal Abstractions Over Multiple Interventional Distributionscited by 21
  10. 2023Causal Deep Learningcited by 20
  11. 2022Causal Structure Discovery between Clusters of Nodes Induced by Latent Factorscited by 18
  12. 2021Causal Modeling With Infinitely Many Variablescited by 7
2 more
  1. 2021Reasoning About Causal Models With Infinitely Many Variablescited by 7
  2. 2023Learning interpretable causal networks from very large datasets, application to 400,000 medical records of breast cancer patientscited by 3

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Citing papers per year: 2024: 1.

1 paper.

  1. 2024Systems with Switching Causal Relations: A Meta-Causal Perspectivecited by 2