Natural Effects in the Presence of an Intermediate Confounder: Evaluation of Pragmatic Estimation Strategies With an Emphasis on the Relationship Between Natural and Interventional Effects
Bibliographic record
Abstract
ABSTRACT Mediation analysis using the so‐called natural effects is an essential tool to uncover causal pathways between an exposure and an outcome. However, natural effects are not generally identified in the presence of an intermediate confounder (), a situation that arguably arises frequently in practice. Three pragmatic approaches can be used to estimate natural effects when such a confounder is present: Natural effects estimators that omit , natural effects estimators that consider as a pre‐exposure confounder, or interventional effects estimators. Interventional effects are analogous to natural effects, but remain identified when is present. The goal of this study was two‐fold: (1) to assess the extent to which natural and interventional estimands differ under a variety of data‐generating mechanisms with intermediate confounding and (2) using simulations, to investigate the corresponding performance of the three aforementioned strategies to estimate natural effects. In the continuous outcome case, using interventional effects estimators was found to be a better analytic strategy for estimating natural effects than using standard natural effects estimators when the interaction term between and in the outcome model was null or moderate in comparison to the other parameters. However, the performance of interventional effects declined as the ‐ interaction was increased. In the binary outcome case, the three estimation strategies yielded more similar results than in the continuous outcome case. The difference between the three analytic strategies is illustrated using data from the World Value Survey.
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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 | 0.106 | 0.384 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".