During data analysis, we are often perplexed by certain disparities observed between two groups of interest within a dataset. To better understand an observed disparity, we need explanations that can pinpoint the data regions where the disparity is most pronounced, along with its causes, i.e., factors that alleviate or exacerbate the disparity. This task can be complex and tedious, particularly when the dataset is large and high-dimensional, demanding an automatic system for discovering explanations (data regions and causes) of an observed disparity in a dataset. When offering explanations for disparities, it is critical that they are not only interpretable but also actionable—enabling users to make informed, data-driven decisions. This requires explanations to go beyond surface-level correlations and instead capture causal relationships. We introduce ExDis, a framework for discovering causal Ex planations for Dis parities between two groups of interest. ExDis identifies data regions (subpopulations) where disparities are most pronounced (or reversed), and associates specific factors that causally contribute to the disparity within each identified data region. We formally define the ExDis framework and the associated optimization problem, analyze its complexity, and develop an efficient algorithm to solve the problem. Through extensive experiments over three real-world datasets, we demonstrate that ExDis generates meaningful causal explanations, outperforms prior methods, and scales effectively to handle large, high-dimensional datasets.
Blau et al. (Thu,) studied this question.