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.
and Xing, Chuanhua , title =
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A Bayesian CP tensor factorization model with Poisson rate for occurrence and conditional Gamma for magnitude, with slice-specific dispersion, applied to 60 million international trade flows to recover multiway dependencies.
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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.
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Bayesian Poisson-Randomized Gamma Tensor Factorization with Application to International Trade Flows
A Bayesian CP tensor factorization model with Poisson rate for occurrence and conditional Gamma for magnitude, with slice-specific dispersion, applied to 60 million international trade flows to recover multiway dependencies.