Develops single-world marginal separable effects as full-population causal estimands for outcomes truncated by death, provides identification and estimation results, and demonstrates them via reanalysis of a prostate cancer trial.
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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.
Causal stability selection produces effect-modifier sets with explicit non-asymptotic false-positive bounds by combining cross-fitted CATE estimation and integrated path stability selection.
The Sinkhorn treatment effect is a new entropic optimal transport measure of divergence between counterfactual distributions that admits first- and second-order pathwise differentiability, debiased estimators, and asymptotically valid tests for distributional treatment effects.
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.
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.
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