Causation and prevention in epidemiology: assumptions, derivations, and measures old and new
Bibliographic record
Abstract
Abstract Epidemiologic measures quantifying the causative or the preventive effect of a particular agent with respect to a given disease are frequently used, but the set of assumptions on which they rest, and the consequences of these assumptions, are not widely understood. We present a rigorous derivation of these measures from the sufficient-causes model of disease occurrence and from the definition of causation as the bringing forward of the occurrence time of an event. This exercise brings out the fact that an understanding of the assumptions underpinning all measures of effect, and of the extent to which they may or may not be met, is necessary to their prudent interpretation. We also introduce a new measure, discarding 1) the sufficient-causes model and 2) the assumption that the agent can only be either causative or preventive, relative to a given disease, but not both. Some may consider this more acceptable than having to decide, on slim or no evidence, that the agent has only one kind of effect on the disease. In any case, I submit that epidemiology should eventually discard the concept of causation, as has been done in some other basic sciences, and replace it with the adequate modeling of disease-producing processes, in individuals and populations.
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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 | 0.029 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".