Causal-INSIGHT: Probing Temporal Models to Extract Causal Structure

Authors: Benjamin Redden, Hui Wang, Shuyan Li Date: 2026-03-26 Paper ID: openalex:2603.25473

Summary

Causal-INSIGHT is presented as a model-agnostic, post-hoc interpretation framework designed to extract directed, time-lagged causal influence structures embedded within trained temporal prediction models. The methodology involves applying systematic, intervention-inspired input clamping during inference and analyzing the resulting model responses to construct influence signals. To refine the output structure, the authors introduce Qbic, a novel criterion that enforces sparsity while preserving predictive fidelity without relying on true causal labels. Experiments across synthetic and real-world benchmarks confirm the framework’s architectural generality and its superior performance in tasks like temporal delay localization.

Key Contributions

• Introduction of Causal-INSIGHT, a model-agnostic, post-hoc framework for extracting model-implied, directed, time-lagged influence structures from trained temporal predictors.
• Development of an inference procedure based on systematic, intervention-inspired input clamping applied at inference time to elicit predictor dependencies.
• Introduction of Qbic, a sparsity-aware graph selection criterion that balances predictive fidelity and structural complexity without requiring ground-truth labels.
• Demonstration that the framework generalizes across diverse backbone architectures and yields significant improvements in temporal delay localization accuracy.

Limitations

The extracted structure reflects the predictor’s implied dependencies (predictor-dependent) rather than necessarily the true data-generating process, and the framework’s efficacy is tied to the quality of the pre-trained predictor.

Open Questions & Future Work

• causal-sufficiency-extension
• multi-variable-intervention-probing
• multi-horizon-predictor-extension

Key Concepts

• causal-insight-probing: A model-agnostic, post-hoc interpretation framework for extracting model-implied, directed, time-lagged influence structure from trained temporal predictors using intervention-inspired input clamping.
• qbic-graph-selection: A sparsity-aware graph selection criterion that balances predictive fidelity against structural complexity when inferring causal structure from predictor responses.

Archivist Review

Archivist review kept only candidates judged central to the paper and reusable across future work. Approved 2 concept(s), 3 open question(s), and 0 dataset(s), with 1 rejected candidate note(s).

Approved Concepts

• Causal-INSIGHT: It is the central model-agnostic framework proposed for extracting causal structure from temporal predictors.
• Qbic Graph Selection Criterion: Qbic is the novel criterion introduced to select the final causal graph structure based on sparsity and predictive fidelity, independent of ground truth.

Approved Open Questions

• Handling Unobserved Confounders: Handling unobserved confounders is a major challenge in structural causal modeling; extending a model-probing technique to address this gap would significantly broaden its practical applicability beyond idealized settings.
• Multi-Variable Intervention Probing: Marginal influence analysis is a limitation for capturing complex, non-linear systems where multivariate causal effects are expected. Generalizing to multi-variable intervention is crucial for a comprehensive interpretation of high-order model dependencies.
• Extension to Multi-Horizon Predictors: Many advanced temporal models, especially in sequence forecasting, are multi-horizon, so extending interpretability to these architectures is necessary for broader utility.

Rejected Candidates

• [open_question] Adaptive Multi-Lag Influence Capture (adaptive-multi-lag-capture) - subcomponent_of_broader_mechanism: This closely related idea about capturing distributed influence is already superseded by the more substantial future work direction of extending to multi-horizon predictors, making it less critical as a standalone question.

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