Selection of mediators and dependence structure for high-dimensional mediation analysis
Bibliographic record
Abstract
Causal mediation analysis examines the potential causal pathways between an exposure variable and outcome through intermediate variables with the goal of estimating direct and indirect effects. In practice, intermediate variables may be high-dimensional, in which case one may first aim to identify the true mediators among them. The dependence structure among mediators may then be studied with the goal of identifying a simple sufficient structure. We propose a two-stage penalized estimation procedure to meet these goals. The first stage involves selecting mediators by identifying non-zero indirect effects via a penalized regression. The second stage aims to simplify the correlation structure among selected mediators enabling the estimation of individual, grouped or joint effects. Through transformation of variables, the correlation selection problem can be reformulated as a standard LASSO problem. The two stages can be performed jointly or sequentially and we study the performance of each implementation through simulation studies. Finally, the proposed approach is applied to a psychiatry study in which the aim is to identify methylation loci that mediate the causal effect of childhood trauma on adult stress level.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".