Breaking Information Granularity Heterogeneity: A Mutual Information-Inspired Causal Discovery Framework for Multi-Rate Time Series
Bibliographic record
Abstract
Causal discovery in multi-rate time series encounters greater challenges compared to regular time series. This stems from a potential problem that has not been noticed and explored in existing studies:information granularity heterogeneity, which refers to the natural difference in information granularity between fast sampling rate data (high information granularity) and slow sampling rate data (low information granularity). Such an imbalance in information granularity can hinder forecasting relationships modeling and induce biased causal learning. Therefore, we propose aMutual Information-iNspired causalDiscovery framework (MIND), aiming to derive rate-agnostic features with consistent information granularity to alleviate information granularity heterogeneity problem. Technically, MIND comprises Stage 1 (pre-training) and Stage 2 (fine-tuning and causal discovery). In Stage 1, empowered by pseudo-slow sampling rate data (generated through the interleaved down sampling strategy) and mutual information, we can eliminate the influence of sampling rates and drive rate-aware encoders (RAEs) to sense key information (i.e., rate-agnostic) that remains unchanged across varying sampling rates. In Stage 2, the well-trained RAEs can extract rate-agnostic features from real multi-rate time series, thus facilitating effective forecasting relationships modeling and yield accurate causal discovery. Empirically, MIND realizes superior performance on various multi-rate scenarios, including four simulation datasets and one real-world dataset.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".