In light of the growing popularity of Exploratory Data Analysis (EDA),
understanding the underlying causes of the knowledge acquired by EDA is
crucial, but remains under-researched. This study promotes for the first time a
transparent and explicable perspective on data analysis, called eXplainable
Data Analysis (XDA). XDA provides data analysis with qualitative and
quantitative explanations of causal and non-causal semantics. This way, XDA
will significantly improve human understanding and confidence in the outcomes
of data analysis, facilitating accurate data interpretation and decision-making
in the real world. For this purpose, we present XInsight, a general framework
for XDA. XInsight is a three-module, end-to-end pipeline designed to extract
causal graphs, translate causal primitives into XDA semantics, and quantify the
quantitative contribution of each explanation to a data fact. XInsight uses a
set of design concepts and optimizations to address the inherent difficulties
associated with integrating causality into XDA. Experiments on synthetic and
real-world datasets as well as human evaluations demonstrate the highly
promising capabilities of XInsight.

本研究首次提出了一种透明且可解释的数据分析方法，称为 eXplainable Data Analysis (XDA)，该方法通过提供定性和定量的因果和非因果语义解释，显著提高了人类对数据分析结果的理解和信心，并为精确数据解释和决策提供了帮助。为此，我们设计了一个名为 XInsight 的通用框架来提取因果图，将因果原语转换为 XDA 语义，并量化每个解释对数据事实的定量贡献。实验证明 XInsight 具有高度的可行性。