4 papers · 1 filter
seq2cause: One Autoregressive Backbone, Four Causal Discovery Tasks in Event Sequences
Hugo Math
Complex systems such as vehicles, patients, or genomes emit discrete event sequences whose operative question is causal, not predictive: which events cause which other events, and…
Your Autoregressive Model Already Reveals the Causal Graph
Hugo Math, Rainer Lienhart
Autoregressive models trained via next-token prediction implicitly learn the conditional independence structure of their data-generating process. We exploit this observation to per…
One-Shot Multi-Label Causal Discovery in High-Dimensional Event Sequences
Hugo Math, Robin Schön, Rainer Lienhart
Understanding causality in event sequences with thousands of sparse event types is critical in domains such as healthcare, cybersecurity, or vehicle diagnostics, yet current method…
Towards Practical Multi-label Causal Discovery in High-Dimensional Event Sequences via One-Shot Graph Aggregation
Hugo Math, Rainer Lienhart
Understanding causality in event sequences where outcome labels such as diseases or system failures arise from preceding events like symptoms or error codes is critical. Yet remain…