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TCD-Arena: Assessing Robustness of Time Series Causal Discovery Methods Against Assumption Violations

arXiv 2605.03045 ↗First posted 4 May 2026cs.LGcited by 0

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Abstract (arXiv)

Causal Discovery (CD) is a powerful framework for scientific inquiry. Yet, its practical adoption is hindered by a reliance on strong, often unverifiable assumptions and a lack of robust performance assessment. To address these limitations and advance empirical CD evaluation, we present TCD-Arena, a modularized, highly customizable, and extendable testing kit to assess the robustness of time series CD algorithms against stepwise more severe assumption violations. For demonstration, we conduct an extensive empirical study comprising around 30 million individual CD attempts and reveal nuanced robustness profiles for 33 distinct assumption violations. Further, we investigate CD ensembles and find that they have the potential to improve general robustness, which has implications for real-world applications. With this, we strive to ultimately facilitate the development of CD methods that are reliable for a diverse range of synthetic and potentially real-world data conditions.

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11 of 87 references.

References · 11

  1. 2017Detecting causal associations in large nonlinear time series datasetscited by 244
  2. 2020High-recall causal discovery for autocorrelated time series with latent confounderscited by 84
  3. 2021Unsuitability of NOTEARS for Causal Graph Discoverycited by 47
  4. 2024Sample, estimate, aggregate: A recipe for causal discovery foundation modelscited by 21
  5. 2024The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applicationscited by 18
  6. 2025TimeGraph: Synthetic Benchmark Datasets for Robust Time-Series Causal Discoverycited by 10
  7. 2025CausalDynamics: A large-scale benchmark for structural discovery of dynamical causal modelscited by 8
  8. 2024Embracing the black box: Heading towards foundation models for causal discovery from time series datacited by 7
  9. 2024Cross-validating causal discovery via Leave-One-Variable-Outcited by 3
  10. 2024OCDB: Revisiting Causal Discovery with a Comprehensive Benchmark and Evaluation Frameworkcited by 2
  11. 2025How PC-based Methods Err: Towards Better Reporting of Assumption Violations and Small Sample Errorscited by 1

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