VAC2: Visual Analysis of Combined Causality in Event Sequences

• URL: http://arxiv.org/abs/2206.05420v1
• Date: Sat, 11 Jun 2022 04:53:23 GMT
• Title: VAC2: Visual Analysis of Combined Causality in Event Sequences
• Authors: Sujia Zhu, Yue Shen, Zihao Zhu, Wang Xia, Baofeng Chang, Ronghua Liang, Guodao Sun
• Abstract summary: We develop a combined causality visual analysis system to help users explore combined causes as well as an individual cause. This interactive system supports multi-level causality exploration with diverse ordering strategies and a focus and context technique. The usefulness and effectiveness of the system are further evaluated by conducting a pilot user study and two case studies on event sequence data.
• Score: 6.145427901944597
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Identifying causality behind complex systems plays a significant role in different domains, such as decision making, policy implementations, and management recommendations. However, existing causality studies on temporal event sequences data mainly focus on individual causal discovery, which is incapable of exploiting combined causality. To fill the absence of combined causes discovery on temporal event sequence data,eliminating and recruiting principles are defined to balance the effectiveness and controllability on cause combinations. We also leverage the Granger causality algorithm based on the reactive point processes to describe impelling or inhibiting behavior patterns among entities. In addition, we design an informative and aesthetic visual metaphor of "electrocircuit" to encode aggregated causality for ensuring our causality visualization is non-overlapping and non-intersecting. Diverse sorting strategies and aggregation layout are also embedded into our parallel-based, directed and weighted hypergraph for illustrating combined causality. Our developed combined causality visual analysis system can help users effectively explore combined causes as well as an individual cause. This interactive system supports multi-level causality exploration with diverse ordering strategies and a focus and context technique to help users obtain different levels of information abstraction. The usefulness and effectiveness of the system are further evaluated by conducting a pilot user study and two case studies on event sequence data.

Related papers

• Causally-Aware Unsupervised Feature Selection Learning [3.8734875101038706]
  Unsupervised feature selection (UFS) has recently gained attention for its effectiveness in processing unlabeled high-dimensional data. Previous graph-based methods fail to account for the differing impacts of non-causal and causal features in constructing the similarity graph. A novel UFS method, called Causally-Aware UnSupErvised Feature Selection learning (CAUSE-FS), is proposed.
  arXiv  Detail & Related papers  (2024-10-16T04:41:38Z)
• Targeted Cause Discovery with Data-Driven Learning [66.86881771339145]
  We propose a novel machine learning approach for inferring causal variables of a target variable from observations. We employ a neural network trained to identify causality through supervised learning on simulated data. Empirical results demonstrate the effectiveness of our method in identifying causal relationships within large-scale gene regulatory networks.
  arXiv  Detail & Related papers  (2024-08-29T02:21:11Z)
• Multi-modal Causal Structure Learning and Root Cause Analysis [67.67578590390907]
  We propose Mulan, a unified multi-modal causal structure learning method for root cause localization. We leverage a log-tailored language model to facilitate log representation learning, converting log sequences into time-series data. We also introduce a novel key performance indicator-aware attention mechanism for assessing modality reliability and co-learning a final causal graph.
  arXiv  Detail & Related papers  (2024-02-04T05:50:38Z)
• VISPUR: Visual Aids for Identifying and Interpreting Spurious Associations in Data-Driven Decisions [8.594140167290098]
  Simpson's paradox is a phenomenon where aggregated and subgroup-level associations contradict with each other. Existing tools provide little insights for humans to locate, reason about, and prevent pitfalls of spurious association in practice. We propose VISPUR, a visual analytic system that provides a causal analysis framework and a human-centric workflow for tackling spurious associations.
  arXiv  Detail & Related papers  (2023-07-26T18:40:07Z)
• Learning latent causal relationships in multiple time series [0.0]
  In many systems, the causal relations are embedded in a latent space that is expressed in the observed data as a linear mixture. A technique for blindly identifying the latent sources is presented. The proposed technique is unsupervised and can be readily applied to any multiple time series to shed light on the causal relationships underlying the data.
  arXiv  Detail & Related papers  (2022-03-21T00:20:06Z)
• Effect Identification in Cluster Causal Diagrams [51.42809552422494]
  We introduce a new type of graphical model called cluster causal diagrams (for short, C-DAGs) C-DAGs allow for the partial specification of relationships among variables based on limited prior knowledge. We develop the foundations and machinery for valid causal inferences over C-DAGs.
  arXiv  Detail & Related papers  (2022-02-22T21:27:31Z)
• Causal Discovery from Sparse Time-Series Data Using Echo State Network [0.0]
  Causal discovery between collections of time-series data can help diagnose causes of symptoms and hopefully prevent faults before they occur. We propose a new system comprised of two parts, the first part fills missing data with a Gaussian Process Regression, and the second part leverages an Echo State Network. We report on their corresponding Matthews Correlation Coefficient(MCC) and Receiver Operating Characteristic curves (ROC) and show that the proposed system outperforms existing algorithms.
  arXiv  Detail & Related papers  (2022-01-09T05:55:47Z)
• Learning Neural Causal Models with Active Interventions [83.44636110899742]
  We introduce an active intervention-targeting mechanism which enables a quick identification of the underlying causal structure of the data-generating process. Our method significantly reduces the required number of interactions compared with random intervention targeting. We demonstrate superior performance on multiple benchmarks from simulated to real-world data.
  arXiv  Detail & Related papers  (2021-09-06T13:10:37Z)
• Visual Causality Analysis of Event Sequence Data [32.74361488457415]
  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.
  arXiv  Detail & Related papers  (2020-09-01T04:28:28Z)
• A Critical View of the Structural Causal Model [89.43277111586258]
  We show that one can identify the cause and the effect without considering their interaction at all. We propose a new adversarial training method that mimics the disentangled structure of the causal model. Our multidimensional method outperforms the literature methods on both synthetic and real world datasets.
  arXiv  Detail & Related papers  (2020-02-23T22:52:28Z)
• End-to-End Models for the Analysis of System 1 and System 2 Interactions based on Eye-Tracking Data [99.00520068425759]
  We propose a computational method, within a modified visual version of the well-known Stroop test, for the identification of different tasks and potential conflicts events. A statistical analysis shows that the selected variables can characterize the variation of attentive load within different scenarios. We show that Machine Learning techniques allow to distinguish between different tasks with a good classification accuracy.
  arXiv  Detail & Related papers  (2020-02-03T17:46:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.