A Weighted Covariate Balancing Method of Estimating Causal Effects in Case-Control Studies
Bibliographic record
Abstract
Propensity score methods have dominated the estimation of treatment effects based on observational data and particularly in the health and medical sciences. We propose a weighting method based on rank-based Mahalanobis distance, namely the covariate balancing rank-based Mahalanobis distance method, to estimate causal effects for observational data. Using Monte Carlo simulations, under different data structures and type of outcome variables, the proposed method is shown to have better performance, in terms of bias reduction and treatment effect estimation. Specifically, under the generalized linear model framework, we simulated datasets based on the Lalonde-PSID study, for linear link function; while datasets were simulated based on the Lindner study, for non-linear link functions. We further apply the proposed method to data extracted from the Nigeria Demographic Health Survey (2013), to investigate the effect of educational exposure on ideal family size among married couples in Nigeria. The proposed method is a viable alternative method that can improve covariates balance, bias reduction, and efficient estimation of treatment effects.
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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 | 0.038 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".