Visual Causality Analysis of Event Sequence Data

@article{Jin2020VisualCA,
  title={Visual Causality Analysis of Event Sequence Data},
  author={Zhuochen Jin and Shunan Guo and Nan Chen and Daniel Weiskopf and David Gotz and Nan Cao},
  journal={IEEE Transactions on Visualization and Computer Graphics},
  year={2020},
  volume={27},
  pages={1343-1352}
}
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… 
[PDF] Semantic Reader
16 Citations
Highly Influential Citations
3
Background Citations
8
Methods Citations
6
Results Citations
1

Figures and Tables from this paper

16 Citations

VAC2: Visual Analysis of Combined Causality in Event Sequences

This developed combined causality visual analysis system can help users effectively explore combined causes as well as an individual cause and supports multi-level causality exploration with diverse ordering strategies and a focus and context technique to help users obtain different levels of information abstraction.

Causal Discovery from Temporal Data: An Overview and New Perspectives

  • Chang GongDi YaoChuzhe ZhangWenbin LiJingping Bi
  • Computer Science
  • 2023
The correlation between the two categories of temporal data casual discovery is specified and a systematical overview of existing solutions is provided and public datasets, evaluation metrics and new perspectives are provided.

Compass: Towards Better Causal Analysis of Urban Time Series

Compass, a novel visual analytics approach for in-depth analyses of the dynamic causality in urban time series, is presented and a tailored multi-dimensional visualization is developed to support the identification of spurious causal relations, thereby improving the reliability of causal analyses.

Causalvis: Visualizations for Causal Inference

Working closely with causal inference experts, this work adopted an iterative design process to develop four interactive visualization modules to support causal inference analysis tasks, and found that Causalvis effectively supported the iterative causal inference process.

ViSRE: A Unified Visual Analysis Dashboard for Proactive Cloud Outage Management

ViSRE is presented: an integrated visual analytics system that integrates causal and predictive models with interactive visualizations to aid in proactive cloud outage management and reduces context switching costs and time spent on manually correlating events during remediation of time-critical outages.

MediCoSpace: Visual Decision-Support for Doctor-Patient Consultations using Medical Concept Spaces from EHRs

The results show that MediCoSpace facilitates a promising, yet complex way to discover unlikely relations and thus suggests a path toward the development of interactive visual tools to provide physicians with more holistic diagnoses and personalized, dynamic treatments for patients.

DOMINO: Visual Causal Reasoning with Time-Dependent Phenomena

This work proposes a set of visual analytics methods that allow humans to participate in the discovery of causal relations associated with windows of time delay and leverages a well-established method, logic-based causality, to enable analysts to test the significance of potential causes and measure their influences toward a certain effect.

A Survey of Approaches for Event Sequence Analysis and Visualization using the ESeVis Framework

This paper develops the Event Sequence Visualization framework (ESeVis) that gives due credit to the traditions of both fields of information visualization and process mining, and provides an integrated perspective on both fields.

Improving Visualization Interpretation Using Counterfactuals

Results indicate that users exposed to counterfactual visualizations formed more careful judgments about feature-to-outcome relationships, and this work introduces a novel visual approach designed to reveal the presence of confounding variables viacounterfactual possibilities during visual data analysis.

Survey on Visual Analysis of Event Sequence Data

This paper reviews the state-of-the-art visual analytics approaches, characterize them with the proposed design space, and categorize them based on analytical tasks and applications, and identifies several remaining research challenges and future research opportunities.

References

SHOWING 1-10 OF 52 REFERENCES

The Visual Causality Analyst: An Interactive Interface for Causal Reasoning

  • Jun WangK. Mueller
  • Computer Science
    IEEE Transactions on Visualization and Computer Graphics
  • 2016
The Visual Causal Analyst is presented - a novel visual causal reasoning framework that allows users to apply their expertise, verify and edit causal links, and collaborate with the causal discovery algorithm to identify a valid causal network.

Visual Causality Analysis Made Practical

  • Jun WangK. Mueller
  • Computer Science
    2017 IEEE Conference on Visual Analytics Science and Technology (VAST)
  • 2017
This paper proposes a comprehensive interface that engages human experts in identifying these subdivisions and allowing them to establish the corresponding causal models via a rich set of interactive facilities, including a new causal network visualization that emphasizes the flow of causal dependencies.

CAUSE: Learning Granger Causality from Event Sequences using Attribution Methods

Across multiple datasets riddled with diverse event interdependency, it is demonstrated that CAUSE achieves superior performance on correctly inferring the inter-type Granger causality over a range of state-of-the-art methods.

DecisionFlow: Visual Analytics for High-Dimensional Temporal Event Sequence Data

The study results demonstrate that DecisionFlow enables the quick and accurate completion of a range of sequence analysis tasks for datasets containing thousands of event types and millions of individual events.

Understanding Indirect Causal Relationships in Node‐Link Graphs

The results of the design study show that sequential ordering does not play a crucial role when analyzing causal relationships, but many connections from/to a variable and higher strength/certainty values may influence the process of finding a root cause and a derived effect.

Detecting causal associations in large nonlinear time series datasets

A novel method that flexibly combines linear or nonlinear conditional independence tests with a causal discovery algorithm that allows to reconstruct causal networks from large-scale time series datasets is introduced.

Visual Progression Analysis of Event Sequence Data

An unsupervised stage analysis algorithm to identify semantically meaningful progression stages as well as the critical events which help define those stages is proposed and a novel visualization system, ET2, is presented to help reveal evolution patterns across stages.

EventThread: Visual Summarization and Stage Analysis of Event Sequence Data

A novel visualization system named EventThread is introduced which clusters event sequences into threads based on tensor analysis and visualizes the latent stage categories and evolution patterns by interactively grouping the threads by similarity into time-specific clusters.

ReactionFlow: an interactive visualization tool for causality analysis in biological pathways

ReactionFlow is introduced, a visual analytics application for pathway analysis that emphasizes the structural and causal relationships amongst proteins, complexes, and biochemical reactions within a given pathway that could be useful to researchers who must be able to understand and analyze the complex nature of biological pathways.

Causal inference in statistics: An overview

This review presents empiricalresearcherswith recent advances in causal inference, and stresses the paradigmatic shifts that must be un- dertaken in moving from traditionalstatistical analysis to…
...