research-article

XInsight: eXplainable Data Analysis Through The Lens of Causality

Authors:

Dongmei ZhangAuthors Info & Claims

Proceedings of the ACM on Management of Data, Volume 1, Issue 2

Article No.: 156, Pages 1 - 27

https://doi.org/10.1145/3589301

Published: 20 June 2023 Publication History

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.

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Cited By

Markakis MZhang ZShahout RGao TLiu CSabek ICafarella M(2024)Press ECCS to Doubt (Your Causal Graph)Proceedings of the Conference on Governance, Understanding and Integration of Data for Effective and Responsible AI10.1145/3665601.3669842(6-15)Online publication date: 9-Jun-2024
https://dl.acm.org/doi/10.1145/3665601.3669842
Youngmann BCafarella MGilad ARoy S(2024)Summarized Causal Explanations For Aggregate ViewsProceedings of the ACM on Management of Data10.1145/36393282:1(1-27)Online publication date: 26-Mar-2024
https://dl.acm.org/doi/10.1145/3639328
Ma PDing RFu QZhang JWang SHan SZhang DBaeza-Yates RBonchi F(2024)Scalable Differentiable Causal Discovery in the Presence of Latent Confounders with Skeleton PosteriorProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3672031(2141-2152)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3672031
Show More Cited By

Index Terms

XInsight: eXplainable Data Analysis Through The Lens of Causality
1. Information systems
  1. Data management systems
  2. Information systems applications
    1. Decision support systems
      1. Data analytics
2. Mathematics of computing
  1. Probability and statistics
    1. Probabilistic representations
      1. Bayesian networks

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Published In

cover image Proceedings of the ACM on Management of Data

Proceedings of the ACM on Management of Data Volume 1, Issue 2

PACMMOD

June 2023

2310 pages

EISSN:2836-6573

DOI:10.1145/3605748

Editor:
Divyakant Agrawal
UC Santa Barbara, United States

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Copyright © 2023 ACM.

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Publication History

Published: 20 June 2023

Published in PACMMOD Volume 1, Issue 2

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Cited By

Markakis MZhang ZShahout RGao TLiu CSabek ICafarella M(2024)Press ECCS to Doubt (Your Causal Graph)Proceedings of the Conference on Governance, Understanding and Integration of Data for Effective and Responsible AI10.1145/3665601.3669842(6-15)Online publication date: 9-Jun-2024
https://dl.acm.org/doi/10.1145/3665601.3669842
Youngmann BCafarella MGilad ARoy S(2024)Summarized Causal Explanations For Aggregate ViewsProceedings of the ACM on Management of Data10.1145/36393282:1(1-27)Online publication date: 26-Mar-2024
https://dl.acm.org/doi/10.1145/3639328
Ma PDing RFu QZhang JWang SHan SZhang DBaeza-Yates RBonchi F(2024)Scalable Differentiable Causal Discovery in the Presence of Latent Confounders with Skeleton PosteriorProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3672031(2141-2152)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3672031
Ma JBhowmick STay LChoi BBarcelo PSanchez-Pi NMeliou ASudarshan S(2024)SIERRA: A Counterfactual Thinking-based Visual Interface for Property Graph Query ConstructionCompanion of the 2024 International Conference on Management of Data10.1145/3626246.3654729(440-443)Online publication date: 9-Jun-2024
https://dl.acm.org/doi/10.1145/3626246.3654729
Ma PJi ZYao PWang SRen KRoychoudhury APaiva AAbreu RStorey M(2024)Enabling Runtime Verification of Causal Discovery Algorithms with Automated Conditional Independence ReasoningProceedings of the IEEE/ACM 46th International Conference on Software Engineering10.1145/3597503.3623348(1-13)Online publication date: 20-May-2024
https://dl.acm.org/doi/10.1145/3597503.3623348
Zhu JCai PNiu BNi ZXu KHuang JWan JMa SWang BZhang DTang LLiu Q(2024)Chat2Query: A Zero-Shot Automatic Exploratory Data Analysis System with Large Language Models2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00420(5429-5432)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00420
Chanson ALabroche NMarcel PPerlata VVassiliadis P(2024)Interestingness Measures for Exploratory Data Analysis: a SurveyNew Trends in Database and Information Systems10.1007/978-3-031-70421-5_2(14-24)Online publication date: 14-Nov-2024
https://doi.org/10.1007/978-3-031-70421-5_2
Sun AMa PYuan YWang SOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Explain any conceptProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3667076(21826-21840)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3667076
Wang ZMa PWang S(2023)Towards Practical Federated Causal Structure LearningMachine Learning and Knowledge Discovery in Databases: Research Track10.1007/978-3-031-43415-0_21(351-367)Online publication date: 18-Sep-2023
https://dl.acm.org/doi/10.1007/978-3-031-43415-0_21

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