Introduces Bayesian Sensitivity Value (BSV) for causal inference sensitivity analysis based on evidence-derived priors and Monte Carlo estimation, applied to diabetes treatment effects.
Causal mediation analysis in economics: Objectives, assumptions, models
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
verdicts
UNVERDICTED 3representative citing papers
An adaptive smoothing method yields valid and asymptotically optimal inference for the mean outcome under an optimal treatment regime, achieving a derived lower bound on asymptotic variance for robust asymptotically linear unbiased estimators regardless of regularity.
A new identification strategy for mediation analysis uses heterogeneous treatment effects to simultaneously identify treatment and mediation effects without addressing some unobserved confounders.
citing papers explorer
-
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.
-
Optimal Inference of the Mean Outcome under Optimal Treatment Regime
An adaptive smoothing method yields valid and asymptotically optimal inference for the mean outcome under an optimal treatment regime, achieving a derived lower bound on asymptotic variance for robust asymptotically linear unbiased estimators regardless of regularity.
-
Extracting Mechanisms from Heterogeneous Effects: An Identification Strategy for Mediation Analysis
A new identification strategy for mediation analysis uses heterogeneous treatment effects to simultaneously identify treatment and mediation effects without addressing some unobserved confounders.