Summarized Causal Explanations For Aggregate Views (Full version)

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 summarized causal explanations for the entire aggregate view. Using background knowledge captured in a causal DAG, CauSumX finds the most effective causal treatments for different groups in the view. We formally define the framework and the optimization problem, study its complexity, and devise an efficient algorithm using the Apriori algorithm, LP rounding, and several optimizations. We experimentally show that our system generates useful summarized causal explanations compared to prior work and scales well for large high-dimensional data