Controlling for Large Sets of Measured Confounders in Mediation Analysis: Comparison of Bayesian Model Averaging, the LASSO, and Path Analysis
Why this work is in the frame
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Bibliographic record
Abstract
Mediation analysis identifies intermediate variables that transmit the effect from an independent (explanatory) variable to the outcome (response) variable. Ignoring relevant confounders can lead to biased estimates of effect in causal mediation analysis. However, including many confounders in the model may increase the variance of estimators. This paper compares the bias, efficiency, power, Type I error rates, and coverage for causal effects in the single mediator model using path analysis, the LASSO, and Bayesian Model Averaging (BMA). Results show that path analysis yielded unbiased estimates with adequate coverage and Type I error rates, and that BMA and the LASSO yielded higher power than path analysis but encountered instances of excessive bias and Type I error rate when confounder effects on the variables are small. An example from the PROsetta Stone project demonstrates the application of all methods.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it