Summarized Causal Explanations For Aggregate Views (Full version)
Brit Youngmann, Michael Cafarella, Amir Gilad, and Sudeepa Roy

TL;DR
CauSumX is a framework that automatically generates concise, causal explanations for aggregate data views, helping users understand the reasons behind the results and enabling better data-driven decisions.
Contribution
It introduces a novel approach to produce summarized causal explanations for aggregate views using causal DAGs, with an efficient algorithm and formal problem analysis.
Findings
Generates useful causal explanations compared to prior methods
Scales effectively to large, high-dimensional datasets
Provides explanations that reflect true causal relationships
Abstract
SQL queries with group-by and average are frequently used and plotted as bar charts in several data analysis applications. Understanding the reasons behind the results in such an aggregate view may be a highly non-trivial and time-consuming task, especially for large datasets with multiple attributes. Hence, generating automated explanations for aggregate views can allow users to gain better insights into the results while saving time in data analysis. When providing explanations for such views, it is paramount to ensure that they are succinct yet comprehensive, reveal different types of insights that hold for different aggregate answers in the view, and, most importantly, they reflect reality and arm users to make informed data-driven decisions, i.e., the explanations do not only consider correlations but are causal. In this paper, we present CauSumX, a framework for generating…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsScientific Computing and Data Management · Bayesian Modeling and Causal Inference · Semantic Web and Ontologies
