Sensitivity Analysis

Sensitivity analysis for observational studies: past, present, and future

Sensitivity analysis via stochastic programming

Sensitivity Analysis with the $R^2$-calculus

A new approach to sensitivity analysis in linear SEMs using stochastic optimization and the $R^2$-calculus

Bounds and semiparametric inference in $L^.Inf$- and $L^2$-sensitivity analysis for observational studies

Sensitivity analysis for observational studies

Sensitivity analysis is widely recognized as a critical step in an observational study but is seldom found in applications. One reason for its underuse is the various forms of model, inference, and interpretation in divergent literatures. This talk …

Bootstrapping sensitivity analysis

ivmodel: An R package for inference and sensitivity analysis of instrumental variables models with one endogenous variable

Vignette of an R package for instrumental variable regression.

Sample-constrained partial identification with application to selection bias

Many partial identification problems can be characterized by the optimal value of a function over a set where both the function and set need to be estimated by empirical data. Despite some progress for convex problems, statistical inference in this …

Selecting and ranking individualized treatment rules with unmeasured confounding

Optimal individualized treatment rules try to assign the best treatment to every individual, but it may be very sensitive to unmeasured confounding bias for groups of people exhibiting small treatment effect in the observational study. We give a …

Sensitivity analysis for inverse probability weighting estimators via the percentile bootstrap

Rosenbaum’s sensitivity analysis framework has several limitations: 1. It is mostly applicable to matched observational studies; 2. It only tests the sharp null hypothesis; 3. It assumes treatment effect homogeneity to obtain a confidence interval of …