The content discusses learning Granger causality from multi-type event sequences using ISAHP, a novel deep learning framework. It highlights the importance of instance-level causal analysis and demonstrates ISAHP's superior performance in discovering complex causal structures compared to classical models and neural point process models.
The paper emphasizes the significance of fine-grained information for decision-making through instance-level causality. It introduces ISAHP as the first neural point process model meeting the requirements of Granger causality, leveraging self-attention mechanisms for alignment with these principles.
ISAHP is shown to outperform baselines in proxy tasks involving type-level causal discovery and instance-level event type prediction. The empirical evaluation showcases its ability to capture synergistic causal effects at both type and instance levels effectively.
Overall, the study presents ISAHP as a promising approach for accurate and robust instance-level causal analysis in asynchronous, interdependent event sequences.
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by Dong... às arxiv.org 03-01-2024
https://arxiv.org/pdf/2402.03726.pdfPerguntas Mais Profundas