Introduces Bayesian Sensitivity Value (BSV) for causal inference sensitivity analysis based on evidence-derived priors and Monte Carlo estimation, applied to diabetes treatment effects.
Journal of the American statistical Association , volume=
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CAFE assesses the fit of observational CATE estimates by partitioning RCT data via propensity scores and comparing to experimental group averages, with theory and extensions for confounders.
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Bayesian Sensitivity of Causal Inference Estimators under Evidence-Based Priors
Introduces Bayesian Sensitivity Value (BSV) for causal inference sensitivity analysis based on evidence-derived priors and Monte Carlo estimation, applied to diabetes treatment effects.
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Assessing Estimate of CATE from Observational Data via an RCT Study
CAFE assesses the fit of observational CATE estimates by partitioning RCT data via propensity scores and comparing to experimental group averages, with theory and extensions for confounders.