  Quantitative analysis of large-scale data is often complicated by the
presence of diverse subgroups, which reduce the accuracy of inferences they
make on held-out data. To address the challenge of heterogeneous data analysis,
we introduce DoGR, a method that discovers latent confounders by simultaneously
partitioning the data into overlapping clusters (disaggregation) and modeling
the behavior within them (regression). When applied to real-world data, our
method discovers meaningful clusters and their characteristic behaviors, thus
giving insight into group differences and their impact on the outcome of
interest. By accounting for latent confounders, our framework facilitates
exploratory analysis of noisy, heterogeneous data and can be used to learn
predictive models that better generalize to new data. We provide the code to
enable others to use DoGR within their data analytic workflows.
