An Adaptive Stacking Ensemble Approach to Network Inference Outperforms Any Single Method
Bibliographic record
Abstract
<title>Abstract</title> This study evaluates both a variety of existing causal inference methods and a variety of ensemble methods. We show that: (i) individual causal network methods vary in their performance across different datasets, so a method that works poorly on one dataset may work well on another; (ii) a Bayesian ensemble method leads overall to better results than using the best single method or any other ensemble method; (iii) for the best results, the Bayesian ensemble method should integrate all methods that satisfy a statistical test of normality on training data. The Bayesian ensemble model easily integrates all kinds of RNA-seq data and priors as well as new and existing inference methods.The paper categorizes and reviews state-of-the-art underlying methods, describes the Bayesian stacking ensemble approach in detail, and presents experimental results. The source code and data used will be available to the community.
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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 | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.000 | 0.004 |
| 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".