DOMINO: Visual Causal Reasoning With Time-Dependent Phenomena

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.

Causalvis: Visualizations for Causal Inference

“…More recently, automatic methods for causal discovery were developed to recover causal structures and learn causal relationships between variables from observational data [19]. These tools can be fully automated, like Causalnex [5], or interactive, like SeqCausal [33] and DOMINO [63], enabling humans to take part in discovering the underlying causal structure from sequential data. However, in both cases, causal discovery tools do not quantify the effect of a treatment on an outcome variable, and require subsequent use of causal inference methods.…”