Learning Granger Causality from Instance-wise Self-attentive Hawkes Processes

Belangrijkste concepten

The author introduces Instance-wise Self-Attentive Hawkes Processes (ISAHP) as a deep learning framework to directly infer Granger causality at the event instance level, addressing the limitations of existing models in capturing complex causal structures.

Samenvatting

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.

Statistieken

Synergy involves 1,000 sequences with 5 event types and sequence lengths ranging from 16,101.
MT dataset comprises 8,703 sequences with 25 event types and sequence lengths up to 90,787.

Citaten

"We propose Instance-wise Self-Attentive Hawkes Processes (ISAHP), a novel deep learning framework that can directly infer the Granger causality at the event instance level."
"ISAHP achieves state-of-the-art performance in proxy tasks involving type-level causal discovery and instance-level event type prediction."

Belangrijkste Inzichten Gedestilleerd Uit

Learning Granger Causality from Instance-wise Self-attentive Hawkes Processes

by Dong... om arxiv.org 03-01-2024

https://arxiv.org/pdf/2402.03726.pdf

Diepere vragen

How does ISAHP's approach to direct instance-level causal analysis impact traditional methods

ISAHP's approach to direct instance-level causal analysis significantly impacts traditional methods by providing a more granular and detailed understanding of causal relationships among individual events. Traditional methods, such as Multivariate Hawkes Processes (MHP) and Neural Point Processes (NPP), often focus on type-level causality analysis, which aggregates information over event sequences. ISAHP, on the other hand, directly infers Granger causality at the event instance level through its additive structure in the intensity function. This allows for a more precise identification of causal relationships between specific events rather than generalized patterns across event types.

What are potential implications of ISAHP's findings on real-world applications beyond machine learning

The findings from ISAHP have broad implications for real-world applications beyond machine learning. By accurately capturing complex synergistic causal effects over multiple event types at the instance level, ISAHP can be applied in various domains such as healthcare, finance, social networks, and cybersecurity. In healthcare, it could aid in early detection and prevention by identifying precursor events that lead to specific symptoms or conditions. In finance, it could enhance risk management strategies by uncovering intricate dependencies between financial events. In social networks and cybersecurity, it could help detect anomalies or predict potential threats based on subtle interactions between different types of events.

How might incorporating additional datasets or varying data characteristics influence ISAHP's performance

Incorporating additional datasets with varying data characteristics can influence ISAHP's performance in several ways:

Data Diversity: Including diverse datasets with different structures and patterns can help validate the robustness and generalizability of ISAHP across various scenarios.
Complexity Handling: More complex datasets may challenge ISAHP to capture intricate causal relationships effectively or require adjustments in model parameters to accommodate diverse data characteristics.
Performance Evaluation: Varying data characteristics may impact how well ISAHP performs compared to traditional methods under different conditions. It provides an opportunity to assess the model's adaptability and scalability.
Model Refinement: Working with varied datasets allows for fine-tuning hyperparameters or introducing new features that optimize performance based on specific dataset attributes.
Real-World Applicability: Datasets reflecting real-world scenarios introduce practical challenges that test ISAHP's applicability outside controlled environments seen in research settings.

By exploring a range of datasets with differing characteristics, researchers can gain insights into how well ISAHP adapts to diverse contexts and refine its capabilities for broader applications beyond theoretical frameworks found in academic studies