Heidegger: Interpretable Temporal Causal Discovery
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Temporal causal discovery aims to find cause-effect relationships between time-series. However, none of the existing techniques is able to identify the causal profile, the temporal pattern that the causal variable needs to follow in order to trigger the most significant change in the outcome. Toward a new horizon, this study introduces the novel problem of Causal Profile Discovery, which is crucial for many applications such as adverse drug reaction and cyber-attack detection. This work correspondingly proposes Heidegger to discover causal profiles, comprised of a flexible randomized block design for hypothesis evaluation and an efficient profile search via on-the-fly graph construction and entropy-based pruning. Heidegger's performance is demonstrated/evaluated extensively on both synthetic and real-world data. The experimental results show the proposed method is robust to noise and flexible at detecting complex patterns.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it