AutoCause: A Python framework that automates expert decisions in environmental time-series causal discovery
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Abstract (arXiv)
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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References · 26
- 2018DAGs with NO TEARS: Continuous Optimization for Structure Learningcited by 752
- 2017Detecting causal associations in large nonlinear time series datasetscited by 244
- 2020DYNOTEARS: Structure Learning from Time-Series Datacited by 200
- 2020Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasetscited by 137
- 2020High-recall causal discovery for autocorrelated time series with latent confounderscited by 84
- 2017Conditional independence testing based on a nearest-neighbor estimator of conditional mutual informationcited by 70
- 2021gCastle: A Python Toolbox for Causal Discoverycited by 61
- 2021Causal Inference for Time series Analysis: Problems, Methods and Evaluationcited by 42
- 2023CausalTime: Realistically Generated Time-series for Benchmarking of Causal Discoverycited by 36
- 2023CUTS: Neural Causal Discovery from Irregular Time-Series Datacited by 33
- 2025CausalRivers -- Scaling up benchmarking of causal discovery for real-world time-seriescited by 26
- 2024The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applicationscited by 18
14 more
- 2023Enhancing Causal Discovery from Robot Sensor Data in Dynamic Scenarioscited by 17
- 2023Causal discovery for time series from multiple datasets with latent contextscited by 16
- 2022CIPCaD-Bench: Continuous Industrial Process datasets for benchmarking Causal Discovery methodscited by 10
- 2025TimeGraph: Synthetic Benchmark Datasets for Robust Time-Series Causal Discoverycited by 10
- 2023Bootstrap aggregation and confidence measures to improve time series causal discoverycited by 8
- 2024Comprehensive Review and Empirical Evaluation of Causal Discovery Algorithms for Numerical Datacited by 8
- 2025CausalDynamics: A large-scale benchmark for structural discovery of dynamical causal modelscited by 8
- 2023Causal discovery in a complex industrial system: A time series benchmarkcited by 7
- 2026Interventional Time Series Priors for Causal Foundation Modelscited by 7
- 2024Are you doing better than random guessing? A call for using negative controls when evaluating causal discovery algorithmscited by 5
- 2024Robust Time Series Causal Discovery for Agent-Based Model Validationcited by 3
- 2024LOCAL: Learning with Orientation Matrix to Infer Causal Structure from Time Series Datacited by 2
- 2026Coarse-to-Fine Learning of Dynamic Causal Structurescited by 2
- 2026VCDF: A Validated Consensus-Driven Framework for Time Series Causal Discoverycited by 1
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