  The collection and visual analysis of large-scale data from complex systems,
such as electronic health records or clickstream data, has become increasingly
common across a wide range of industries. This type of retrospective visual
analysis, however, is prone to a variety of selection bias effects, especially
for high-dimensional data where only a subset of dimensions is visualized at
any given time. The risk of selection bias is even higher when analysts
dynamically apply filters or perform grouping operations during ad hoc
analyses. These bias effects threatens the validity and generalizability of
insights discovered during visual analysis as the basis for decision making.
Past work has focused on bias transparency, helping users understand when
selection bias may have occurred. However, countering the effects of selection
bias via bias mitigation is typically left for the user to accomplish as a
separate process. Dynamic reweighting (DR) is a novel computational approach to
selection bias mitigation that helps users craft bias-corrected visualizations.
This paper describes the DR workflow, introduces key DR visualization designs,
and presents statistical methods that support the DR process. Use cases from
the medical domain, as well as findings from domain expert user interviews, are
also reported.
