Visual Analytics and Imaging Laboratory (VAI Lab)
Computer Science Department, Stony Brook University, NY
Abstract: Current work on using visual analytics to determine causal relations among variables has mostly been based on the
concept of counterfactuals. As such the derived static causal networks do not take into account the effect of time as an indicator.
However, knowing the time delay of a causal relation can be crucial as it instructs how and when actions should be taken. Yet, similar to
static causality, deriving causal relations from observational time-series data, as opposed to designed experiments, is not a
straightforward process. It can greatly benefit from human insight to break ties and resolve errors. We hence propose a set of visual
analytics methods that allow humans to participate in the discovery of causal relations associated with windows of time delay.
Specifically, we leverage 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. Furthermore, since an effect can be a cause of other effects, we allow
users to aggregate different temporal cause-effect relations found with our method into a visual flow diagram to enable the discovery of
temporal causal networks. To demonstrate the effectiveness of our methods we constructed a prototype system named DOMINO and
showcase it via a number of case studies using real-world datasets. Finally, we also used DOMINO to conduct several evaluations with
human analysts from different science domains in order to gain feedback on the utility of our system in practical scenarios.
Teaser: The below shows the the DOMINO interface analyzing the Air Quality dataset:
The interface consists of (A) the conditional distribution view for manually exploring potential causes of the specified effect, (B) the causal inference panel for the interactive analysis of causal relations under different time delays and significance thresholds, (C) the time sequence view for examining the synchronized time series, and (D) the causal flow chart displaying the established causal relations.
Video: Watch it to get a quick overview:
Paper: J. Wang, K. Mueller, "DOMINO: Visual Causal Reasoning with Time-Dependent Phenomena," IEEE Trans. on Visualization and Computer Graphics, (to appear) 2023 PDF PPT