Bibliographic record
Abstract
Abstract Causation is a concept that is universally intuitive, but it is difficult to define and even more difficult to create clear guidelines for inferring it from data. Although much of science is devoted to inferring causation, it is generally accepted that causation cannot be directly observed because doing so would require observing mutually contradictory states of the world. In epidemiology and other social sciences, causal inference can be particularly difficult, and there is widespread misunderstanding of how to interpret evidence for causation. Several conceptualizations and graphical models, including causal response types, causal pie models, and causal pathway diagrams, have been developed to aid in this process. These models can be used to better understand quantitative effect measures and the concepts of confounding and probability. Clearly defining and modeling causation leads to a recognition of some myths about causal inference (e.g., that randomized trials are a “gold standard” or that cause‐effect relations can be identified using “causal criteria”), and reveals how research can be designed to be most useful in inferring causation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.017 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.030 | 0.005 |
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".