Characterizing Qualitative Causal Dependency for NAT-modeled Bayesian Networks
Bibliographic record
Abstract
The Non-impeding noisy-AND Tree (NAT) model is a local model we focus on in Bayesian networks (BNs). In order to improve algorithms for learning structures of NAT-modeled BNs, further understanding of dependency between a cause and the effect within a NAT model is required. For example, how does the dependence vary with the location of a causal event and values of single-causals? Although dependence between them can be measured by mutual information computed using algorithms for NAT conditional probability distributions (CPDs), it does not address directly what conditions of NAT models render the dependence stronger or weaker at the macro level. \n \nA computational framework is developed in this research for analyzing qualitative dependency in the NAT-modeled BNs. The framework involves reducing the exponential space of NAT structures to a tractable subspace through chain NATs, converting NATs to equivalent chain NATs exactly and qualitatively, and conducting a qualitative dependency analysis for chain NATs by the approach with imprecise probabilities. Our experiment demonstrates the feasibility of qualitative estimation of dependency through this framework. The qualitative analysis helps identify the most influential aspects of causal dependency in the NAT models.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".