Abstract:Country-level temporal panels are widely used in empirical analysis. Researchers often need to audit how different entities respond to historical signals over different time horizons. Current approaches typically do not provide directly auditable entity-specific lag summaries. We formulate entity-conditioned heterogeneous lag discovery as a temporal panel mining task and propose AC-GATE, an Adaptive-Conditioning Encoder with a Scale-Invariant Lag Gate. It instantiates conditional Moderated Distributed Lag by using observable entity-level proxies to condition lag-weight distributions over historical observations, thereby making effective lags structural outputs of the model rather than post-hoc explanations. The evaluation is based on a layered audit protocol that separates predictive calibration from lag discovery. A synthetic panel with known ground-truth lags is used for mechanism recovery testing, and two real-world country-level panels are used for external audit and stress testing. The results show that AC-GATE can recover heterogeneous lag structure in synthetic data, and generates non-degenerate, externally structured effective lags in real data.
| Comments: | Preprint/technical paper. An interpretable neural audit framework for entity-conditioned lag discovery in panel time series. 10 pages, 5 figures, 16 tables. Code available at the GitHub repository |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.21542 [cs.LG] |
| (or arXiv:2605.21542v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.21542 arXiv-issued DOI via DataCite |
From: Andi Xu [view email]
[v1]
Wed, 20 May 2026 09:09:15 UTC (3,934 KB)
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