Introduces Bayesian Sensitivity Value (BSV) for causal inference sensitivity analysis based on evidence-derived priors and Monte Carlo estimation, applied to diabetes treatment effects.
Biometrics , volume=
8 Pith papers cite this work. Polarity classification is still indexing.
8
Pith papers citing it
years
2026 8verdicts
UNVERDICTED 8representative citing papers
A conditional adaptive perturbation approach enables valid in-sample inference for machine learning-identified subgroups with nonregular boundaries via triple robustness.
Matrix-weighted regularization for robust multi-task regression achieves optimal MSE under weaker spectral assumptions and performs no worse than independent learning when balancedness is poor.
UD-DML creates balanced representative subsamples via uniform design in PCA space for efficient double machine learning estimation of average treatment effects on large datasets.
A doubly robust, asymptotically normal estimator for regression with completely missing covariates across populations, combining importance weighting and moment imputation under a sub-population shift assumption.
Data equity, prediction equity, and decision equity are distinct statistical requirements that need separate evaluations to address how racial biases in pulse oximetry measurements lead to treatment disparities.
PEQ-Net uses policy-aware reparameterization of ICE Q-functions and kernel mean embeddings in a shared encoder, followed by LTMLE, to jointly estimate multiple policies while constraining second-order bias for lower variance.
A copula-based adjustment makes doubly robust treatment effect estimates consistent when endogenous variables correlate with errors, while keeping the double robustness property.
citing papers explorer
No citing papers match the current filters.