One-Shot Multi-Label Causal Discovery in High-Dimensional Event Sequences

• URL: http://arxiv.org/abs/2509.23213v1
• Date: Sat, 27 Sep 2025 09:49:26 GMT
• Title: One-Shot Multi-Label Causal Discovery in High-Dimensional Event Sequences
• Authors: Hugo Math, Robin Schön, Rainer Lienhart,
• Abstract summary: We present OSCAR, a one-shot causal autoregressive method that infers per-sequence Markov Boundaries.<n>On a real-world automotive dataset with 29,100 events and 474 labels, OSCAR recovers interpretable causal structures in minutes.
• Score: 20.072624123275528
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Understanding causality in event sequences with thousands of sparse event types is critical in domains such as healthcare, cybersecurity, or vehicle diagnostics, yet current methods fail to scale. We present OSCAR, a one-shot causal autoregressive method that infers per-sequence Markov Boundaries using two pretrained Transformers as density estimators. This enables efficient, parallel causal discovery without costly global CI testing. On a real-world automotive dataset with 29,100 events and 474 labels, OSCAR recovers interpretable causal structures in minutes, while classical methods fail to scale, enabling practical scientific diagnostics at production scale.

Related papers

• TRACE: Scalable Amortized Causal Discovery from Single Sequences via Autoregressive Density Estimation [14.409508347156397]
  We study causal discovery from a single observed sequence of discrete events generated by a process.<n>We introduce TRACE, a scalable framework that repurposes autoregressive models as pretrained density estimators for conditional mutual information estimation.
  arXiv  Detail & Related papers  (2026-02-01T10:18:27Z)
• Towards Practical Multi-label Causal Discovery in High-Dimensional Event Sequences via One-Shot Graph Aggregation [14.409508347156397]
  CARGO is a scalable multi-label causal discovery method for sparse, high-dimensional event sequences.<n>It 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.<n>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.
  arXiv  Detail & Related papers  (2025-09-23T14:58:50Z)
• Large-Scale Targeted Cause Discovery with Data-Driven Learning [66.86881771339145]
  We propose a novel machine learning approach for inferring causal variables of a target variable from observations.<n>By employing a local-inference strategy, our approach scales with linear complexity in the number of variables, efficiently scaling up to thousands of variables.<n> Empirical results demonstrate superior performance in identifying causal relationships within large-scale gene regulatory networks.
  arXiv  Detail & Related papers  (2024-08-29T02:21:11Z)
• Multi-scale Fusion Fault Diagnosis Method Based on Two-Dimensionaliztion Sequence in Complex Scenarios [0.0]
  Rolling bearings are critical components in rotating machinery, and their faults can cause severe damage. Early detection of abnormalities is crucial to prevent catastrophic accidents. Traditional and intelligent methods have been used to analyze time series data, but in real-life scenarios, sensor data is often noisy and cannot be accurately characterized in the time domain. This paper proposes an improved convolutional neural network method with a multi-scale feature fusion model and deep learning compression techniques for deployment in industrial scenarios.
  arXiv  Detail & Related papers  (2023-04-11T13:05:50Z)
• Abnormal Event Detection via Hypergraph Contrastive Learning [54.80429341415227]
  Abnormal event detection plays an important role in many real applications. In this paper, we study the unsupervised abnormal event detection problem in Attributed Heterogeneous Information Network. A novel hypergraph contrastive learning method, named AEHCL, is proposed to fully capture abnormal event patterns.
  arXiv  Detail & Related papers  (2023-04-02T08:23:20Z)
• A Bi-LSTM Autoencoder Framework for Anomaly Detection -- A Case Study of a Wind Power Dataset [2.094022863940315]
  Anomalies refer to data points or events that deviate from normal and homogeneous events. This study presents a novel framework for time series anomaly detection using a combination of Bi-LSTM architecture and Autoencoder. The Bi-LSTM Autoencoder model achieved a classification accuracy of 96.79% and outperformed more commonly used LSTM Autoencoder models.
  arXiv  Detail & Related papers  (2023-03-17T00:24:28Z)
• Are we certain it's anomalous? [57.729669157989235]
  Anomaly detection in time series is a complex task since anomalies are rare due to highly non-linear temporal correlations. Here we propose the novel use of Hyperbolic uncertainty for Anomaly Detection (HypAD) HypAD learns self-supervisedly to reconstruct the input signal.
  arXiv  Detail & Related papers  (2022-11-16T21:31:39Z)
• BaCaDI: Bayesian Causal Discovery with Unknown Interventions [118.93754590721173]
  BaCaDI operates in the continuous space of latent probabilistic representations of both causal structures and interventions. In experiments on synthetic causal discovery tasks and simulated gene-expression data, BaCaDI outperforms related methods in identifying causal structures and intervention targets.
  arXiv  Detail & Related papers  (2022-06-03T16:25:48Z)
• A Deep Learning Approach for Active Anomaly Detection of Extragalactic Transients [1.7152709285783647]
  We present a variational recurrent autoencoder neural network to encode simulated Rubin Observatory extragalactic transient events. We rank 1,129,184 events based on an anomaly score estimated using an isolation forest. Our algorithm is able to identify these transients as anomalous well before peak, enabling real-time follow up studies.
  arXiv  Detail & Related papers  (2021-03-22T18:02:19Z)
• Recomposition vs. Prediction: A Novel Anomaly Detection for Discrete Events Based On Autoencoder [5.781280693720236]
  One of the most challenging problems in the field of intrusion detection is anomaly detection for discrete event logs. We propose DabLog, a Deep Autoencoder-Based anomaly detection method for discrete event Logs. Our approach determines whether a sequence is normal or abnormal by analyzing (encoding) and reconstructing (decoding) the given sequence.
  arXiv  Detail & Related papers  (2020-12-27T16:31:05Z)
• TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks [73.01104041298031]
  TadGAN is an unsupervised anomaly detection approach built on Generative Adversarial Networks (GANs) To capture the temporal correlations of time series, we use LSTM Recurrent Neural Networks as base models for Generators and Critics. To demonstrate the performance and generalizability of our approach, we test several anomaly scoring techniques and report the best-suited one.
  arXiv  Detail & Related papers  (2020-09-16T15:52:04Z)
• Multi-Scale One-Class Recurrent Neural Networks for Discrete Event Sequence Anomaly Detection [63.825781848587376]
  We propose OC4Seq, a one-class recurrent neural network for detecting anomalies in discrete event sequences. Specifically, OC4Seq embeds the discrete event sequences into latent spaces, where anomalies can be easily detected.
  arXiv  Detail & Related papers  (2020-08-31T04:48:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.