A semiparametric data fusion framework restores identification of mediation effects from separately observed mediator and outcome data using shared IVs under unmeasured confounding and no-interaction plus latent alignment conditions.
arXiv preprint arXiv:2210.00200 , year=
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
fields
stat.ME 4years
2026 4verdicts
UNVERDICTED 4representative citing papers
dShrink is a model-free transfer estimator using summary statistics that is guaranteed to have lower expected quadratic error than the target-only estimator under arbitrary population heterogeneity.
Tutorial on a statistical roadmap and R packages for selective borrowing in hybrid controlled trials, demonstrated on synthetic lung cancer data.
A review organizes externally controlled trial methodology through causal estimands and identifiability assumptions for single-arm and hybrid designs with borrowing strategies.
citing papers explorer
-
Semiparametric Mediation Analysis with Separately Observed Mediator and Outcome under Unmeasured Confounding
A semiparametric data fusion framework restores identification of mediation effects from separately observed mediator and outcome data using shared IVs under unmeasured confounding and no-interaction plus latent alignment conditions.
-
Divide-and-shrink: An efficient and heterogeneity-agnostic approach for transfer estimation using summary statistics
dShrink is a model-free transfer estimator using summary statistics that is guaranteed to have lower expected quadratic error than the target-only estimator under arbitrary population heterogeneity.
-
Robust Estimation and Inference with Selective Borrowing in Hybrid Controlled Trials: A Tutorial with SelectiveIntegrative and intFRT
Tutorial on a statistical roadmap and R packages for selective borrowing in hybrid controlled trials, demonstrated on synthetic lung cancer data.
-
Externally Controlled Trials: A Review of Design and Borrowing Through a Causal Lens
A review organizes externally controlled trial methodology through causal estimands and identifiability assumptions for single-arm and hybrid designs with borrowing strategies.