Causal Discovery That Drops the Regular-Lag Assumption for Irregular Time Series

arXiv·medium signal

Martim 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.