Towards Practical Multi-label Causal Discovery in High-Dimensional Event Sequences via One-Shot Graph Aggregation

Hugo Math, Rainer Lienhart

Keywords: causal discovery, event sequences, multi-label causal discovery, density estimation, time-series, structure learning

TL;DR: We introduce CARGO, a scalable multi-label causal discovery framework to perform interpretable decision making in high-dimensional event sequences using structural fusion of one-shot graphs.

Abstract:

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 remains an unsolved challenge across domains like healthcare or vehicle diagnostics. We introduce CARGO, a scalable multi-label causal discovery method for sparse, high-dimensional event sequences comprising of thousands of unique event types. Using two pretrained causal Transformers as domain-specific foundation models for event sequences—CARGO infers in parallel, per sequence one-shot causal graphs and aggregates them using an adaptive frequency fusion to reconstruct the global Markov boundaries of labels. This two-stage approach enables efficient probabilistic reasoning at scale while bypassing the intractable cost of full-dataset conditional independence testing.
Our results on a challenging real-world automotive fault prediction dataset with over 29,100 unique event types and 474 imbalanced labels demonstrate CARGO’s ability to perform structured reasoning.

Submission Number: 4