Invited Commentary: Boundless Science—Putting Natural Direct and Indirect Effects in a Clearer Empirical Context: Figure 1.
Bibliographic record
Abstract
Epidemiologists are increasingly using natural effects for applied mediation analyses, yet 1 key identifying assumption is unintuitive and subject to some controversy. In this issue of the Journal, Jiang and VanderWeele (Am J Epidemiol. 2015;182(2):105-108) formalize the conditions under which the difference method can be used to estimate natural indirect effects. In this commentary, I discuss implications of the controversial "cross-worlds" independence assumption needed to identify natural effects. I argue that with a binary mediator, a simple modification of the authors' approach will provide bounds for natural direct and indirect effect estimates that better reflect the capacity of the available data to support empirical statements on the presence of mediated effects. I discuss complications encountered when odds ratios are used to decompose effects, as well as the implications of incorrectly assuming the absence of exposure-induced mediator-outcome confounders. I note that the former problem can be entirely resolved using collapsible measures of effect, such as risk ratios. In the Appendix, I use previous derivations for natural direct effect bounds on the risk difference scale to provide bounds on the odds ratio scale that accommodate 1) uncertainty due to the cross-world independence assumption and 2) uncertainty due to the cross-world independence assumption and the presence of exposure-induced mediator-outcome confounders.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".