Studentized Sensitivity Analysis for the Sample Average Treatment Effect in Paired Observational Studies

Studentized sensitivity analysis for the sample average treatment effect in paired observational studies

A fundamental limitation of causal inference in observational studies is that perceived evidence for an effect might instead be explained by factors not accounted for in the primary analysis. Methods for assessing the sensitivity of a study’s conclusions to unmeasured confounding have been established under the assumption that the treatment effect is constant across all individuals. In the pote...

Sensitivity Analysis in Observational Studies

Sensitivity analysis for average treatment effects

Based on the conditional independence or unconfoundedness assumption, matching has become a popular approach to estimate average treatment effects. Checking the sensitivity of the estimated results with respect to deviations from this identifying assumption has become an increasingly important topic in the applied evaluation literature. If there are unobserved variables that affect assignment i...

mhbounds - Sensitivity Analysis for Average Treatment Effects

mhbounds – Sensitivity Analysis for Average Treatment Effects Matching has become a popular approach to estimate average treatment effects. It is based on the conditional independence or unconfoundedness assumption. Checking the sensitivity of the estimated results with respect to deviations from this identifying assumption has become an increasingly important topic in the applied evaluation li...

Sensitivity analysis for mistakenly adjusting for mediators in estimating total effect in observational studies

OBJECTIVES In observational studies, epidemiologists often attempt to estimate the total effect of an exposure on an outcome of interest. However, when the underlying diagram is unknown and limited knowledge is available, dissecting bias performances is essential to estimating the total effect of an exposure on an outcome when mistakenly adjusting for mediators under logistic regression. Throug...