# VAC2: Visual Analysis of Combined Causality in Event Sequences

arXiv: https://arxiv.org/abs/2206.05420 · HTML: https://mlsanity.com/paper/2206.05420/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 58 references that are in the map.
- First posted on arXiv 11 Jun 2022.

## Builds on (most cited first, 15 of 15)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Learning Granger Causality for Hawkes Processes](https://mlsanity.com/paper/1602.04511/) (2016)
- [High-dimensional consistency in score-based and hybrid structure learning](https://mlsanity.com/paper/1507.02608/) (2015)
- [A Survey of Learning Causality with Data: Problems and Methods](https://mlsanity.com/paper/1809.09337/) (2018)
- [Visual Causality Analysis of Event Sequence Data](https://mlsanity.com/paper/2009.00219/) (2020)
- [A Visual Analytics Approach for Exploratory Causal Analysis: Exploration, Validation, and Applications](https://mlsanity.com/paper/2009.02458/) (2020)
- [Illusion of Causality in Visualized Data](https://mlsanity.com/paper/1908.00215/) (2019)
- [Directed hypergraph neural network](https://mlsanity.com/paper/2008.03626/) (2020)
- [Visual Analytics for Temporal Hypergraph Model Exploration](https://mlsanity.com/paper/2008.07299/) (2020)
- [Causal Support: Modeling Causal Inferences with Visualizations](https://mlsanity.com/paper/2107.13485/) (2021)
- [Towards a Survey on Static and Dynamic Hypergraph Visualizations](https://mlsanity.com/paper/2107.13936/) (2021)
- [CausalFlow: Visual Analytics of Causality in Event Sequences](https://mlsanity.com/paper/2008.11899/) (2020)
- [Mining Combined Causes in Large Data Sets](https://mlsanity.com/paper/1508.07092/) (2015)
- [Optimizing Video Caching at the Edge: A Hybrid Multi-Point Process Approach](https://mlsanity.com/paper/2102.00646/) (2021)
- [Automatic Polygon Layout for Primal-Dual Visualization of Hypergraphs](https://mlsanity.com/paper/2108.00671/) (2021)

## Built on by (most cited first, 2 of 2)

- [Causal Discovery from Temporal Data: An Overview and New Perspectives](https://mlsanity.com/paper/2303.10112/) (2023)
- [C5: Towards Better Conversation Comprehension and Contextual Continuity for ChatGPT](https://mlsanity.com/paper/2308.05567/) (2023)

## Abstract (arXiv)

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.