Exploratory visual analysis of missing data for multiple imputation

Missing data pose a widely known challenge for multivariate data analysis. Imputation is a mechanism for replacing missing values with plausible substitutes. Multiple imputation is often used to generate several plausible datasets for a wide range of statistical models to create robust standard errors and reduce potential bias from complete case analyses. Implementing an imputation algorithm requires several decisions about settings to generate meaningful imputed datasets. The analysis tasks can be summarized as analyzing attribute relations, analyzing the missingness mechanisms, supporting the selection of imputation parameters, and analyzing the imputation outcome. We propose a visual analytics pipeline that supports decision-making in each of the required steps. Each step is supported by novel visual encodings or existing ones that are adapted to the task. In particular, we adapt all methods to operate on heterogeneous data. Key visualizations are a 2D embedding of attribute similarities for heterogeneous data, a heatmap visualization providing a scalable overview of the effect of missingness on data distributions between all pairs of attributes (and between all original and all imputed attributes), and extended parallel coordinates that can handle both heterogeneous and missing data. Moreover, we propose a novel scheme for computing the imputation order based on an interactive predictor graph visualization. We illustrate the effectiveness of our visual encodings and the interactive visual analytics pipeline within workflows executed with domain experts from epidemiology to analyze cohort studies. The domain experts reported increased confidence in the imputation result due to improved transparency.