Scalable Sample-Level Causal Discovery in Event Sequences via Autoregressive Density Estimation

ArXi:2602.01135v2 Announce Type: replace We study causal discovery from a single observed sequence of discrete events generated by a stochastic process, as encountered in vehicle logs, manufacturing systems, or patient trajectories. This regime is particularly challenging due to the absence of repeated samples, high dimensionality, and long-range temporal dependencies of the single observation during inference. We