发表机构
ISR-Lisbon, Instituto Superior Técnico; Digital Scholarship at Oxford, University of Oxford; Jet Propulsion Lab., Caltech（里斯本信号与系统研究所，里斯本高等理工学院; 牛津大学牛津数字学术中心; 加州理工学院喷气推进实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
AutoCause是一款开源Python框架，通过封装四种因果发现方法、添加参考模型并分级链接，实现环境时间序列因果发现中专家决策的自动化，提升分析的可审计性与可重复性。
AI 中文摘要
环境时间序列因果发现需要专家对方法选择、条件独立性检验、滞后范围、样本量充足性、多重检验控制及证据解释做出决策。这些决策在不同数据集间应用不一致，导致生成的因果图无法比较、复现或审计。我们提出AutoCause，这是一个开源Python工作流，可记录每一项决策，通过扩展因果审计模块生成默认设置，同时允许领域知识驱动的覆写。该工作流封装了来自三个家族的四种成熟因果发现方法，添加了非因果参考模型，并按方法计数对因果链接进行分级。在来自DGP-Atlas、TimeGraph及拓扑衍生的CausalRivers参考的145个数据集上，各方法恢复了参考图的互补部分。在合成基准上，多数方法支持的链接比单一方法的链接更精确，但针对河流拓扑的情况并非如此。AutoCause将不一致的专家实践转化为可审计、可重复的分析，因果解释仍由分析人员负责。代码可在此httpsURL获取。
英文摘要
Environmental time-series causal discovery requires expert decisions about method choice, conditional-independence tests, lag horizons, sample-size adequacy, multiple-testing control, and evidence interpretation. Applied inconsistently across datasets, these choices yield graphs that cannot be compared, reproduced, or audited. We present AutoCause, an open-source Python workflow that records each decision, derives defaults from an extended causal-audit module, and admits domain-informed overrides. The workflow wraps four established causal-discovery methods from three families, adds non-causal reference models, and grades links by method-count support. On 145 datasets from DGP-Atlas, TimeGraph, and a topology-derived CausalRivers reference, the methods recover complementary parts of the reference graphs. Majority-supported links are more precise than single-method links on the synthetic benchmarks but not against river topology. AutoCause converts inconsistent expert practice into an auditable, repeatable analysis; causal interpretation remains with the analyst. Available at https://github.com/marcoruizrueda/autocause.
Comments33 pages, 10 figures. Submitted to Environmental Modelling & Software
