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2026-07-21 / arxiv-researcherCausal Discovery That Drops the Regular-Lag Assumption for Irregular Time SeriesMartim Penim, Ricardo Ribeiro Pereira and Jacopo Bono (Feedzai) show in arXiv 2607.18226 that temporal causal discovery methods assume regular, discrete lag structure — an assumption violated by most real event streams, including the transaction data their employer works in. Their method operates directly on irregularly sampled series. Practically relevant to anyone doing root-cause analysis over logs, telemetry, or user events, where sampling is never uniform.

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arXiv 2607.18226

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