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  • CausalRivers -- Scaling up benchmarking of causal discovery for real . . .
    To bridge this gap, we introduce CausalRivers, the largest in-the-wild causal discovery benchmarking kit for time-series data to date CausalRivers features an extensive dataset on river discharge that covers the eastern German territory (666 measurement stations) and the state of Bavaria (494 measurement stations)
  • GitHub - CausalRivers causalrivers: The CausalRivers benchmark package . . .
    CausalRivers Scaling up benchmarking of causal discovery for real-world time-series This is the official repository of CausalRivers, the largest real-world Causal Discovery benchmark for time series to this date Also check our Website where we will maintain the current leaderboard
  • CausalRivers - Scaling up benchmarking of causal discovery for real . . .
    CausalRivers - Scaling up benchmarking of causal discovery for real-world time-series Causal discovery, or identifying causal relationships from observational data, is a notoriously challenging task, with numerous methods proposed to tackle it Despite this, in-the-wild evaluation is still lacking, as works frequently rely on synthetic data evaluation and sparse real-world examples under
  • CausalRivers - Scaling up benchmarking of causal discovery for real . . .
    Abstract Causal discovery, or identifying causal relationships from observational data, is a notoriously challenging task, with numerous methods proposed to tackle it Despite this, in-the-wild evaluation of these methods is still lacking, as works frequently rely on synthetic data evaluation and sparse real-world examples under critical theoretical assumptions Real-world causal structures
  • ICLR2025 Soptlight |CausalRivers：因果发现新基准，全球最大真实时间序列数据集来袭！
    论文名称： CausalRivers - Scaling up benchmarking of causal discovery for real-world time-series 论文作者： Gideon Stein, Maha Shadaydeh, Jan Blunk, Niklas Penzel, Joachim Denzler
  • CausalRivers -- Scaling up benchmarking of causal discovery for real . . .
    To bridge this gap, we introduce CausalRivers, the largest in-the-wild causal discovery benchmarking kit for time-series data to date
  • CausalRivers - Scaling up benchmarking of causal discovery for real . . .
    CausalRivers is introduced, the largest in-the-wild causal discovery benchmarking kit for time-series data to date, and several causal discovery approaches are evaluated through a set of experiments to identify areas for improvement Causal discovery, or identifying causal relationships from observational data, is a notoriously challenging task, with numerous methods proposed to tackle it
  • CausalRivers：时间序列因果关系发现benchmark - 知乎
    这篇发表于 ICLR 2025 的论文推出了CausalRivers，当前规模最大的真实世界时间序列因果发现基准套件，为领域注入了 “贴近实战” 的评估新范式。 CausalRivers 用1160个 水文站 的4年高频数据，第一次为河流建立了"社交关系网"，作为一个benchmark，可以供大家验证因果推理算法。 为何需要 CausalRivers？ 多数
  • CausalRivers -- Scaling up benchmarking of causal discovery for real . . .
    TL;DR CausalRivers delivers the largest real-world benchmarking kit for causal discovery on time-series, using extensive river-discharge data with ground-truth graphs to stress-test methods under conditions like high dimensionality, non-stationarity, and distributional shifts
  • CausalRivers -- Scaling up benchmarking of causal discovery for real . . .
    Conclusion CausalRivers represents a significant step forward in benchmarking causal discovery methods by providing large-scale, real-world datasets with known ground truth The benchmark exposes a concerning gap between theoretical capabilities and practical performance of current algorithms when faced with the complexities of natural systems





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