Observational studies are a widely used and challenging class of studies. A key challenge is selecting a study cohort from the available data, or "pruning" the data, in a way that produces both sufficient balance in pre-treatment covariates and an easily described cohort from which results can be generalized. Although many techniques for pruning exist, it can be difficult for analysts using these methods to see how the cohort is being selected. Consequently, these methods are underutilized in research. Visual Pruner is a free, easy-to-use Shiny web application that can improve both the credibility and the transparency of observational studies by letting analysts use updatable linked visual displays of estimated propensity scores and important baseline covariates to refine inclusion criteria. By helping researchers see how the pre-treatment covariate distributions in their data relate to the estimated probabilities of treatment assignment (propensity scores), the app lets researchers make pruning decisions based on covariate patterns that are otherwise hard to discover. The app yields a set of inclusion criteria that can be used in conjunction with further statistical analysis in R or any other statistical software. While the app is interactive and allows iterative decision-making, it can also easily be incorporated into a reproducible research workflow. Visual Pruner is currently hosted by the Vanderbilt Department of Biostatistics and can also be run locally within R or RStudio. For links and additional resources, see http://biostat.mc.vanderbilt.edu/VisualPruner.