Pre-processing data to reduce biases: full matching incorporating an instrumental variable in population-based studies
Bibliographic record
Abstract
BACKGROUND: Epidemiologists are often concerned with unobserved biases that produce confounding in population-based studies. We introduce a new design approach-'full matching incorporating an instrumental variable (IV)' or 'Full-IV Matching'-and illustrate its utility in reducing observed and unobserved biases to increase inference accuracy. Our motivating example is tailored to a central question in humanitarian emergencies-the difference in sexual violence risk by displacement setting. METHODS: We conducted a series of 1000 Monte Carlo simulations generated from a population-based survey after the 2010 Haitian earthquake and included earthquake damage severity as an IV and the unmeasured variable of 'social capital'. We compared standardized mean differences (SMDs) for covariates after different designs to understand potential biases. Mean risk differences (RDs) were used to assess each design's accuracy in estimating the oracle of the simulated data set. RESULTS: Naive analysis and pair matching equivalently performed. Full matching reduced imbalances between exposed and comparison groups across covariates, except for the unobserved covariate of 'social capital'. Pair and full matching overstated differences in sexual violence risk when displaced to a camp vs a community [pair: RD = 0.13, 95% simulation interval (SI) 0.09-0.16; full: RD = 0.11, 95% SI 0.08-0.14). Full-IV Matching reduced imbalances across observed covariates and importantly 'social capital'. The estimated risk difference (RD = 0.07, 95% SI 0.03-0.11) was closest to the oracle (RD = 0.06, 95% SI 0.4-0.8). CONCLUSION: Full-IV Matching is a novel approach that is promising for increasing inference accuracy when unmeasured sources of bias likely exist.
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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 | 0.153 | 0.343 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".