XInsight: eXplainable Data Analysis Through The Lens of Causality

TL;DR

XInsight introduces a comprehensive framework for explainable data analysis by integrating causality, providing transparent, causal, and non-causal explanations to enhance understanding and decision-making.

Contribution

It presents XInsight, a novel end-to-end pipeline that extracts causal graphs and quantifies explanations, advancing the interpretability of data analysis through causality integration.

Findings

Effective causal graph extraction demonstrated on datasets

Enhanced human understanding through explanations

User study confirms improved confidence in analysis results

Abstract

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.