AutoCause: A Python framework that automates expert decisions in environmental time-series causal discovery

Abstract: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 this https URL.

33 pages, 10 figures. Submitted to Environmental Modelling & Software

Machine Learning (cs.LG)

arXiv:2608.00198 [cs.LG]

(or arXiv:2608.00198v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2608.00198 Focus to learn more arXiv-issued DOI via DataCite

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