Causal-INSIGHT: Probing Temporal Models to Extract Causal Structure
This provides a method for interpreting complex temporal models in fields like dynamical systems, but it is incremental as it builds on existing interpretation techniques.
The authors tackled the problem of interpreting temporal models by developing Causal-INSIGHT, a framework that extracts directed, time-lagged influence structures from pre-trained predictors, resulting in competitive structural accuracy and significant improvements in temporal delay localization.
Understanding directed temporal interactions in multivariate time series is essential for interpreting complex dynamical systems and the predictive models trained on them. We present Causal-INSIGHT, a model-agnostic, post-hoc interpretation framework for extracting model-implied (predictor-dependent), directed, time-lagged influence structure from trained temporal predictors. Rather than inferring causal structure at the level of the data-generating process, Causal-INSIGHT analyzes how a fixed, pre-trained predictor responds to systematic, intervention-inspired input clamping applied at inference time. From these responses, we construct directed temporal influence signals that reflect the dependencies the predictor relies on for prediction, and introduce Qbic, a sparsity-aware graph selection criterion that balances predictive fidelity and structural complexity without requiring ground-truth graph labels. Experiments across synthetic, simulated, and realistic benchmarks show that Causal-INSIGHT generalizes across diverse backbone architectures, maintains competitive structural accuracy, and yields significant improvements in temporal delay localization when applied to existing predictors.