A semi-parametric framework using fractional imputation and EM algorithm for estimating causal direct and indirect effects with left-censored mediators due to assay limits.
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Copula parameterization of potential outcome dependence enables point identification, rate-doubly-robust estimation, and sensitivity analysis for causal effects with ordinal outcomes under unconfoundedness.
Proposes influence function projection exploiting graphical independence constraints for more efficient semiparametric estimation of bounds on average causal effects under sensitivity models for unmeasured confounding.
Derives new analytical sample size and power formulas for marginal hazard ratios in causal inference with time-to-event outcomes, applicable to randomized trials and observational studies via IPW estimators.
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.
The MQIV model identifies the ATT via a modified Wald ratio under a multiplicative treatment model that permits direct effects of the quasi-instrument on the outcome.
The work gives conditions favoring complete-case over IPW estimators in federated settings with missing data and introduces a multi-model calibrated weighting estimator that is consistent when at least one candidate model is correct at each site.
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