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arXiv Statistics ML By Huiyang Yi, Xiaojian Shen, Yonggang Wu, Duxin Chen, He Wang, Wenwu Yu

CausalCompass: Evaluating the Robustness of Time-Series Causal Discovery in Misspecified Scenarios

Read the original on arXiv Statistics ML →

CausalCompass is a benchmark framework that evaluates the robustness of time‑series causal discovery (TSCD) methods when modeling assumptions are violated. The authors benchmark several TSCD algorithms across eight assumption‑violation scenarios, finding that no single method dominates all settings but deep learning approaches generally perform best. They also analyze hyperparameter sensitivity, conduct ablation studies, and highlight the impact of preprocessing on methods like NTS‑NOTEARS.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Statistics ML.

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