Causal-INSIGHT: Probing Temporal Models to Extract Causal Structure

• URL: http://arxiv.org/abs/2603.25473v1
• Date: Thu, 26 Mar 2026 14:16:37 GMT
• Title: Causal-INSIGHT: Probing Temporal Models to Extract Causal Structure
• Abstract summary: Causal-INSIGHT is a model-agnostic, post-hoc interpretation framework for extracting model-implied (predictor-dependent) directed, time-lagged influence structure from trained temporal predictors.<n>We introduce Qbic, a sparsity-aware graph selection criterion that balances predictive fidelity and structural complexity without requiring ground-truth graph labels.
• Score: 4.718641165826564
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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