Nearly all statistical analyses that inform policy-making are based on imperfect data. As examples, the data may suffer from measurement errors, missing values, sample selection bias, or record linkage errors. Analysts have to decide how to handle such data imperfections, e.g., analyze only the complete cases or impute values for the missing items via some posited model. Their choices can influence estimates and hence, ultimately, policy decisions. Thus, it is prudent for analysts to evaluate the sensitivity of estimates and policy decisions to the assumptions underlying their choices. To facilitate this goal, we propose that analysts define metrics and visualizations that target the sensitivity of the ultimate decision to the assumptions underlying their approach to handling the data imperfections. Using these visualizations, the analyst can assess their confidence in the policy decision under their chosen analysis. We illustrate metrics and corresponding visualizations with two examples, namely considering possible measurement error in the inputs of predictive models of presidential vote share and imputing missing values when evaluating the percentage of children exposed to high levels of lead.
Wadekar et al. (Wed,) studied this question.