Author

Mohammad Fesanghary

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#machine learning Preprint Sep 2026

LUCID: Learning Under Confounding for Inference and Discovery in Time Series

Unobserved common causes are pervasive in real-world time series and can induce spurious associations that causal discovery methods mistake for direct edges. We propose LUCID (Learning Under Confounding for Inference and Discovery, a regime-adaptive deconfounding layer that first estimates the confounding regime from d...

Mohammad Fesanghary · 0 citations

Jul 2026

Causal-TS: A Python Library for Causal Discovery in High-Dimensional and Nonstationary Time Series

Causal-TS provides four specialized algorithms-CDNOTS, CDNOTS+, CEDAR, and GRACE-along with wrappers for GES, Granger, LASSO-VAR, and LGES, all sharing a unified conditional independence test layer with GPU acceleration via PyTorch.

Mohammad Fesanghary · arXiv.org · 1 citation