XInsight: eXplainable Data Analysis Through The Lens of Causality
This addresses the need for interpretability in data analysis for users in real-world decision-making, though it appears incremental as it builds on existing EDA and causality concepts.
The study tackled the problem of making exploratory data analysis (EDA) more transparent and explainable by introducing XInsight, a framework for explainable data analysis (XDA) that provides causal and non-causal explanations, resulting in improved human understanding and confidence in data analysis outcomes.
In light of the growing popularity of Exploratory Data Analysis (EDA), understanding the underlying causes of the knowledge acquired by EDA is crucial. However, it remains under-researched. This study promotes a transparent and explicable perspective on data analysis, called eXplainable Data Analysis (XDA). For this reason, we present XInsight, a general framework for XDA. XInsight provides data analysis with qualitative and quantitative explanations of causal and non-causal semantics. This way, it will significantly improve human understanding and confidence in the outcomes of data analysis, facilitating accurate data interpretation and decision making in the real world. 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 a user study demonstrate the highly promising capabilities of XInsight.