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.
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.
A doubly cross-fit DR sieve learner identifies and estimates within-stratum heterogeneous treatment effects under principal ignorability and odds-ratio sensitivity, with oracle rates and uniform bands.
PLANE jointly estimates latent gene positions from a target network and proxy embeddings on a larger gene set, with provably optimal channel weighting and demonstrated gains in network recovery and imputation.
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.
citing papers explorer
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Causal Inference for All: Marginal Estimands for Outcomes Truncated by Death
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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Evaluating causal indirect effects when mediators are left-censored by assay limit of quantification
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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Sinkhorn Treatment Effects: A Causal Optimal Transport Measure
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.
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Doubly cross-fit debiased machine learning of heterogeneous treatment effects under principal stratification
A doubly cross-fit DR sieve learner identifies and estimates within-stratum heterogeneous treatment effects under principal ignorability and odds-ratio sensitivity, with oracle rates and uniform bands.
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AI-Augmented Statistical Network Estimation with Proxy Gene Embeddings
PLANE jointly estimates latent gene positions from a target network and proxy embeddings on a larger gene set, with provably optimal channel weighting and demonstrated gains in network recovery and imputation.
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Exploiting independence constraints for efficient estimation of bounds on causal effects in the presence of unmeasured confounding
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.
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Augmented transfer regression learning for completely missing covariates
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.
- Causal Stability Selection