AI RESEARCH
TCD-Arena: Assessing Robustness of Time Series Causal Discovery Methods Against Assumption Violations
arXiv CS.LG
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ArXi:2605.03045v1 Announce Type: new 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 severe assumption violations.