AutoCause 自动化环境时间序列因果发现中的专家决策，提升可复现性。
AutoCause: A Python framework that automates expert decisions in environmental time-series causal discovery
- 自动记录并推荐因果推断的每一步决策，支持领域知识干预。
- 在145个数据集上验证，多数方法支持的因果边更精确。
- 适合需要可审计、可重复因果分析的研究者使用。
环境时间序列因果发现依赖专家对方法选择、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等决策。这些决策在不同数据集间应用不一致，导致结果不可比较、不可复现、无法审计。我们提出 AutoCause，一个开源 Python 工作流，记录每一步决策，通过扩展的因果审计模块生成默认值，并允许领域知识覆盖。该工作流整合了来自三个家族的四种成熟因果发现方法，加入非因果参考模型，并按方法支持数对因果链接进行评分。在来自 DGP-Atlas、TimeGraph 及拓扑推导的 CausalRivers 参考数据集共145个数据集上，各方法恢复了参考图的不同部分。多数方法支持的链接在合成基准上比单方法链接更精确，但在河流拓扑上未体现优势。AutoCause 将不一致的专家实践转化为可审计、可复现的分析流程，因果解释权仍归分析者所有。代码已公开于 https://github.com/marcoruizrueda/autocause。
原文摘要 · Abstract (English)
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.
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