# XInsight: eXplainable Data Analysis Through The Lens of Causality

arXiv: https://arxiv.org/abs/2207.12718 · HTML: https://mlsanity.com/paper/2207.12718/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 72 references that are in the map.
- First posted on arXiv 26 Jul 2022.

## Builds on (most cited first, 15 of 15)

- [Explaining Black-Box Algorithms Using Probabilistic Contrastive Counterfactuals](https://mlsanity.com/paper/2103.11972/) (2021)
- [Measuring the Effect of Training Data on Deep Learning Predictions via Randomized Experiments](https://mlsanity.com/paper/2206.10013/) (2022)
- [Constraint-based Causal Discovery from Multiple Interventions over Overlapping Variable Sets](https://mlsanity.com/paper/1403.2150/) (2014)
- [Local Explanations via Necessity and Sufficiency: Unifying Theory and Practice](https://mlsanity.com/paper/2103.14651/) (2021)
- [Causality-based Explanation of Classification Outcomes](https://mlsanity.com/paper/2003.06868/) (2020)
- [Complaint-driven Training Data Debugging for Query 2.0](https://mlsanity.com/paper/2004.05722/) (2020)
- [MacroBase: Prioritizing Attention in Fast Data](https://mlsanity.com/paper/1603.00567/) (2016)
- [Causal Relational Learning](https://mlsanity.com/paper/2004.03644/) (2020)
- [ML4C: Seeing Causality Through Latent Vicinity](https://mlsanity.com/paper/2110.00637/) (2021)
- [Causality-Guided Adaptive Interventional Debugging](https://mlsanity.com/paper/2003.09539/) (2020)
- [PerfCE: Performance Debugging on Databases with Chaos Engineering-Enhanced Causality Analysis](https://mlsanity.com/paper/2207.08369/) (2022)
- [Score-Based Explanations in Data Management and Machine Learning](https://mlsanity.com/paper/2007.12799/) (2020)
- [Causal Perception in Question-Answering Systems](https://mlsanity.com/paper/2012.14477/) (2020)
- [Explaining Inference Queries with Bayesian Optimization](https://mlsanity.com/paper/2102.05308/) (2021)
- [Putting Things into Context: Rich Explanations for Query Answers using Join Graphs (extended version)](https://mlsanity.com/paper/2103.15797/) (2021)

## Built on by (most cited first, 9 of 9)

- [Explain Any Concept: Segment Anything Meets Concept-Based Explanation](https://mlsanity.com/paper/2305.10289/) (2023)
- [Demonstration of InsightPilot: An LLM-Empowered Automated Data Exploration System](https://mlsanity.com/paper/2304.00477/) (2023)
- [Towards Practical Federated Causal Structure Learning](https://mlsanity.com/paper/2306.09433/) (2023)
- [Causality-Aided Trade-off Analysis for Machine Learning Fairness](https://mlsanity.com/paper/2305.13057/) (2023)
- [Scalable Differentiable Causal Discovery in the Presence of Latent Confounders with Skeleton Posterior (Extended Version)](https://mlsanity.com/paper/2406.10537/) (2024)
- [Enabling Runtime Verification of Causal Discovery Algorithms with Automated Conditional Independence Reasoning (Extended Version)](https://mlsanity.com/paper/2309.05264/) (2023)
- [Towards Automated Cross-domain Exploratory Data Analysis through Large Language Models](https://mlsanity.com/paper/2412.07214/) (2024)
- [KAHAN: Knowledge-Augmented Hierarchical Analysis and Narration for Financial Data Narration](https://mlsanity.com/paper/2509.17037/) (2025)
- [Stress-Testing Causal Claims via Cardinality Repairs](https://mlsanity.com/paper/2512.02491/) (2025)

## Abstract (arXiv)

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.