Discovery of causality and acausality from temporal sequential data
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
In this thesis, we present a solution to the problem of discovering rules from sequential data. As part of the solution, the Temporal Investigation Method for Enregistered Record Sequences (TIMERS) and its implementation, the TimeSleuth software, are introduced. TIMERS uses the passage of time between attribute observations as justification for judging the causality of a rule set. Given a sorted sequence of input data records, and assuming that the effects take time to manifest themselves, we merge the input records to bring potential causes and effects together in the same record. Three tests are performed using three different assumptions on the nature of the relationship: instantaneous, causal, or acausal. The temporal reversibility of a relationship in time is used to judge the relationship as potentially acausal, while reversibility is considered as evidence for judging the relationship as potentially causal. To visualise the attributes' influence on each other, the thesis introduces dependence diagrams, which are graphs that connect condition attributes to decision attributes. We performed a series of comparisons between TIMERS and other causality discoverers, and also experimented with both synthetic and real temporal data for the discovery of temporal rules. The results show an improvement in the quality of the rules discovered with TIMERS.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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