Visual Causality Analysis of Event Sequence Data

TL;DR

This paper presents a visual analytics system that extends Granger causality analysis on Hawkes processes, enabling interactive exploration, verification, and refinement of causal relationships in event sequence data with user feedback.

Contribution

It introduces a novel visual analytics framework that combines causal modeling with user feedback to improve interpretability and accuracy in causal analysis of complex event sequences.

Findings

User feedback improves causal model accuracy.

System supports iterative causal exploration and refinement.

Qualitative case studies demonstrate system usefulness.

Abstract

Causality is crucial to understanding the mechanisms behind complex systems and making decisions that lead to intended outcomes. Event sequence data is widely collected from many real-world processes, such as electronic health records, web clickstreams, and financial transactions, which transmit a great deal of information reflecting the causal relations among event types. Unfortunately, recovering causalities from observational event sequences is challenging, as the heterogeneous and high-dimensional event variables are often connected to rather complex underlying event excitation mechanisms that are hard to infer from limited observations. Many existing automated causal analysis techniques suffer from poor explainability and fail to include an adequate amount of human knowledge. In this paper, we introduce a visual analytics method for recovering causalities in event sequence data. We extend the Granger causality analysis algorithm on Hawkes processes to incorporate user feedback into causal model refinement. The visualization system includes an interactive causal analysis framework that supports bottom-up causal exploration, iterative causal verification and refinement, and causal comparison through a set of novel visualizations and interactions. We report two forms of evaluation: a quantitative evaluation of the model improvements resulting from the user-feedback mechanism, and a qualitative evaluation through case studies in different application domains to demonstrate the usefulness of the system.