A centered Gaussian with smaller covariance assigns at least as much probability as one with larger covariance to any closed convex set with reference probability at least 1/2.
Blocking estimators and inference under the Neyman-Rubin model
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
abstract
We derive the variances of estimators for sample average treatment effects under the Neyman-Rubin potential outcomes model for arbitrary blocking assignments and an arbitrary number of treatments.
years
2026 2representative citing papers
Stochastic sensitivity analysis for matched studies finds worst-case conditional laws for hidden confounders instead of worst-case realizations, controlled by a sensitivity parameter that permits imperfect alignment with potential outcomes and yields higher robustness than conventional methods.
citing papers explorer
-
Gaussian comparison above the median
A centered Gaussian with smaller covariance assigns at least as much probability as one with larger covariance to any closed convex set with reference probability at least 1/2.
-
Stochastic Sensitivity Analysis for Matched Observational Studies
Stochastic sensitivity analysis for matched studies finds worst-case conditional laws for hidden confounders instead of worst-case realizations, controlled by a sensitivity parameter that permits imperfect alignment with potential outcomes and yields higher robustness than conventional methods.