Detecting and Explaining Causes From Text For a Time Series Event
This addresses the challenge of explaining events for applications requiring interpretable causal analysis, though it appears incremental in combining existing methods.
The paper tackles the problem of generating explanations for time series events by detecting causal relationships between time series and textual data, and constructing explanatory chains; it shows empirical evidence of successfully extracting meaningful causality and generating appropriate explanations.
Explaining underlying causes or effects about events is a challenging but valuable task. We define a novel problem of generating explanations of a time series event by (1) searching cause and effect relationships of the time series with textual data and (2) constructing a connecting chain between them to generate an explanation. To detect causal features from text, we propose a novel method based on the Granger causality of time series between features extracted from text such as N-grams, topics, sentiments, and their composition. The generation of the sequence of causal entities requires a commonsense causative knowledge base with efficient reasoning. To ensure good interpretability and appropriate lexical usage we combine symbolic and neural representations, using a neural reasoning algorithm trained on commonsense causal tuples to predict the next cause step. Our quantitative and human analysis show empirical evidence that our method successfully extracts meaningful causality relationships between time series with textual features and generates appropriate explanation between them.