TippingSens: an R Shiny application to facilitate sensitivity analysis for causal inference under confounding

Most strategies for causal inference based on quasi-experimental or observational data critically rely on the assumption of unconfoundedness. If this assumption is suspect, sensitivity analysis can be a viable tool to evaluate the impact of confounding on the analysis of interest. One of the earliest proposals for such a sensitivity analysis was suggested by Rosenbaum/ Rubin (1983). However, while it is straightforward to obtain estimates for the causal effect under specific assumptions regarding an unobserved confounder, conducting a full sensitivity analysis based on a range of parameter settings is unwieldy based on the simple forking tables which Rosenbaum and Rubin used. To tackle the multiple parameter problem of the Rosenbaum-Rubin approach, we present an interactive R Shiny application called TippingSens, which visualizes the impact of various parameter settings on the estimated causal effect. Borrowing from the literature on tipping point analysis, the flexible app facilitates manipulating all parameters simultaneously. We demonstrate the usefulness of our app by conducting a sensitivity analysis for a quasi-experiment measuring the effect of vocational training programs on unemployed men. The online supplement accompanying this paper provides a step-by-step introduction to the app using the original illustrative example from Rosenbaum/Rubin (1983).