mlsanityData to 7 Oct 2026

CausalMan: A physics-based simulator for large-scale causality

arXiv 2502.12707 ↗First posted 18 Feb 2025cs.LG stat.MLcited by 4

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Abstract (arXiv)

A comprehensive understanding of causality is critical for navigating and operating within today's complex real-world systems. The absence of realistic causal models with known data generating processes complicates fair benchmarking. In this paper, we present the CausalMan simulator, modeled after a real-world production line. The simulator features a diverse range of linear and non-linear mechanisms and challenging-to-predict behaviors, such as discrete mode changes. We demonstrate the inadequacy of many state-of-the-art approaches and analyze the significant differences in their performance and tractability, both in terms of runtime and memory complexity. As a contribution, we will release the CausalMan large-scale simulator. We present two derived datasets, and perform an extensive evaluation of both.

Key facts. Cited by 4 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 18 Feb 2025.

Builds on

36 of 67 references.

References · 36

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24 more
  1. 2024Robust agents learn causal world modelscited by 81
  2. 2018Graphical Models for Processing Missing Datacited by 57
  3. 2022Neural Causal Models for Counterfactual Identification and Estimationcited by 54
  4. 2022Interventions, Where and How? Experimental Design for Causal Models at Scalecited by 49
  5. 2023Causal normalizing flows: from theory to practicecited by 49
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  7. 2021Unsuitability of NOTEARS for Causal Graph Discoverycited by 46
  8. 2018Benchmarking Framework for Performance-Evaluation of Causal Inference Analysiscited by 33
  9. 2019Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluationcited by 32
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  12. 2022Evaluation Methods and Measures for Causal Learning Algorithmscited by 23
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  17. 2013Structural Intervention Distance (SID) for Evaluating Causal Graphscited by 14
  18. 2013On the Equivalence of Causal Modelscited by 13
  19. 2024The Causal Chambers: Real Physical Systems as a Testbed for AI Methodologycited by 12
  20. 2024Separation-based distance measures for causal graphscited by 9
  21. 2023Causal discovery in a complex industrial system: A time series benchmarkcited by 7
  22. 2024Challenges and Considerations in the Evaluation of Bayesian Causal Discoverycited by 5
  23. 2024Scalable and Flexible Causal Discovery with an Efficient Test for Adjacencycited by 5
  24. 2023On the Unlikelihood of D-Separationcited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 4.

4 papers.

  1. 2026ORCA: An End-to-End Interactive Copilot for Optimized Root Cause Analysiscited by 0
  2. 2026StableRCA: Robust Graph-Agnostic Mechanism-Level Root Cause Analysiscited by 0
  3. 2026CausalSteward: An Agentic Divide-Conquer-Combine Copilot for Causal Discoverycited by 0
  4. 2026CAFE: Counterfactual Prediction via Fast Posterior Estimationcited by 0