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

ArXi:2602.07915v2 Announce Type: replace-cross Causal discovery from time series is a fundamental task in machine learning. However, its widespread adoption is hindered by a reliance on untestable causal assumptions and by the lack of robustness-oriented evaluation in existing benchmarks. To address these challenges, we propose CausalCompass, a flexible and extensible benchmark framework designed to assess the robustness of time-series causal discovery (TSCD) methods under violations of modeling assumptions.