mlsanityData to 14 Oct 2026

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

arXiv 2509.19112 ↗First posted 23 Sep 2025cs.LG cs.AIcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 22 of its 55 references that are in the map. First posted on arXiv 23 Sep 2025.

Builds on

22 of 55 references.

References · 22

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10 more
  1. 2023Structural Hawkes Processes for Learning Causal Structure from Discrete-Time Event Sequencescited by 14
  2. 2022Robust Expected Information Gain for Optimal Bayesian Experimental Design Using Ambiguity Setscited by 12
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  6. 2024Harnessing Event Sensory Data for Error Pattern Prediction in Vehicles: A Language Model Approachcited by 6
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 4.

4 papers.

  1. 2026Your Autoregressive Model Already Reveals the Causal Graphcited by 2
  2. 2026Transforming Vehicle Diagnostics: A Multimodal Approach to Error Patterns Predictioncited by 0
  3. 2026Multi-Agent Causal Reasoning System for Error Pattern Rule Automation in Vehiclescited by 0
  4. 2026seq2cause: One Autoregressive Backbone, Four Causal Discovery Tasks in Event Sequencescited by 0