Proceedings of the UAI 2014 Workshop Causal Inference: Learning and Prediction:co-located with 30th Conference on Uncertainty in Artificial Intelligence (UAI 2014) : Quebec City, Canada, July 27, 2014
Bibliographic record
Abstract
This workshop is the third in a series of UAI workshops on the topic of causality, following up on two successful predecessors, the UAI Workshop on Causal Structure Learning 2012 and the Approaches to Causal Structure Learning Workshop, UAI 2013.The aim of this workshop was to bring together researchers interested in the challenges of causal inference from observational and interventional data, especially when confounding variables, feedback loops or selection bias may be present.For this workshop, we decided to extend the scope from causal structure learning to include methods for making causal predictions, i.e., for predicting what happens under interventions.We especially encouraged contributions describing practical applications of causal methods.There were 8 submissions, all full-length papers, each of which was peer-reviewed by two or three program committee members.We accepted five of these for oral presentation and for inclusion in these proceedings.The proceedings also include abstracts for three invited talks, including the two key-note talks by Robert Spekkens and Elias Bareinboim.Slides of most of the oral presentations are available on the workshop website:https://staff.fnwi.uva.nl/j.m.mooij/uai2014-causality-workshop/index.htmlWe would like to thank the paper authors and presenters for their contributions and the program committee members for their reviewing service.We also appreciate the organizational support of the main UAI 2014 conference, in particular we would like to thank John Mark Agosta,
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.069 | 0.025 |
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".