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.

本文介绍了一种用于从事件序列数据中恢复因果关系的可视化分析方法，它已将 Hawkes 过程的 Granger 因果分析算法扩展到包括用户反馈的因果模型细化，并具有一组新颖的可视化和交互。通过一系列的定量和定性评估以不同的应用领域中的案例研究来展示这一系统的实用性。

事件序列数据的视觉因果分析

Visual Causality Analysis of Event Sequence Data

Behavioral economics tells us that emotions can profoundly affect individual
behavior and decision-making. Does this also apply to societies at large, i.e.,
can societies experience mood states that affect their collective decision
making? By extension is the public mood correlated or even predictive of
economic indicators? Here we investigate whether measurements of collective
mood states derived from large-scale Twitter feeds are correlated to the value
of the Dow Jones Industrial Average (DJIA) over time. We analyze the text
content of daily Twitter feeds by two mood tracking tools, namely OpinionFinder
that measures positive vs. negative mood and Google-Profile of Mood States
(GPOMS) that measures mood in terms of 6 dimensions (Calm, Alert, Sure, Vital,
Kind, and Happy). We cross-validate the resulting mood time series by comparing
their ability to detect the public's response to the presidential election and
Thanksgiving day in 2008. A Granger causality analysis and a Self-Organizing
Fuzzy Neural Network are then used to investigate the hypothesis that public
mood states, as measured by the OpinionFinder and GPOMS mood time series, are
predictive of changes in DJIA closing values. Our results indicate that the
accuracy of DJIA predictions can be significantly improved by the inclusion of
specific public mood dimensions but not others. We find an accuracy of 87.6% in
predicting the daily up and down changes in the closing values of the DJIA and
a reduction of the Mean Average Percentage Error by more than 6%.

本文通过对大规模 Twitter Feed 情绪的追踪分析，发现公众情绪状态对 Dow Jones 工业平均指数 (DJIA) 未来走势有显著预测作用，其中一些情绪维度会带来更高的 DJIA 预测准确率。