Using Causal Threads to Explain Changes in a Dynamic System
This work addresses the need for verifiable explanations in dynamic systems, such as geological theories, but is incremental as it builds on existing causal modeling approaches.
The paper tackles the problem of explaining state changes in dynamic systems by developing structured causal models, specifically process-based dynamic knowledge graphs, and presents an early prototype interface for visualization, contrasting with statistical methods like LLMs by offering inspectable representations.
We explore developing rich semantic models of systems. Specifically, we consider structured causal explanations about state changes in those systems. Essentially, we are developing process-based dynamic knowledge graphs. As an example, we construct a model of the causal threads for geological changes proposed by the Snowball Earth theory. Further, we describe an early prototype of a graphical interface to present the explanations. Unlike statistical approaches to summarization and explanation such as Large Language Models (LLMs), our approach of direct representation can be inspected and verified directly.