XInsight: eXplainable Data Analysis Through The Lens of Causality
Summary: XInsight enables eXplainable Data Analysis (XDA) via causality in EDA. A three-module pipeline: causal graph extraction, XDA-semantics translation, and explanation attribution, addresses integration challenges; experiments and user study validate its promise. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Pingchuan Ma
- 2. Rui Ding
- 3. Shuai Wang
- 4. Shi Han
- 5. Dongmei Zhang
Incoming Citations (Sorted by Pagerank)
Showing 10 of 10 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,172 | Summarized Causal Explanations For Aggregate Views | 2024 | SIGMOD | 4.8114797e-05 |
| 9,644 | Fair and Actionable Causal Prescription Ruleset | 2025 | SIGMOD | 4.3109001e-05 |
| 9,871 | From Logs to Causal Inference: Diagnosing Large Systems | 2025 | VLDB | 4.2667743e-05 |
| 10,101 | Privacy-preserving and Verifiable Causal Prescriptive Analytics | 2026 | SIGMOD | 4.1945683e-05 |
| 10,147 | Causal Explanations for Disparate Trends: Where and Why? | 2026 | SIGMOD | 4.1945683e-05 |
| 10,213 | Stress-Testing Causal Claims via Cardinality Repairs | 2026 | SIGMOD | 4.1945683e-05 |
| 10,429 | CauSumX: Summarized Causal Explanations For Group-By-Average Queries | 2025 | SIGMOD | 4.1945683e-05 |
| 10,740 | Finding Convincing Views to Endorse a Claim | 2025 | VLDB | 4.1945683e-05 |
| 10,784 | Towards Automated Cross-domain Exploratory Data Analysis through Large Language Models | 2025 | VLDB | 4.1945683e-05 |
| 10,875 | SDEcho: Efficient Explanation of Aggregated Sequence Difference | 2025 | VLDB | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 23 of 23 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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Semantically Similar Papers
| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,429 | CauSumX: Summarized Causal Explanations For Group-By-Average Queries | 2025 | SIGMOD | 4.1945683e-05 |
| 10,147 | Causal Explanations for Disparate Trends: Where and Why? | 2026 | SIGMOD | 4.1945683e-05 |
| 10,427 | CausaLens: A System for Summarizing Causal DAGs | 2025 | SIGMOD | 4.1945683e-05 |
| 7,364 | ExplainED: Explanations for EDA Notebooks | 2020 | VLDB | 4.7519211e-05 |
| 7,172 | Summarized Causal Explanations For Aggregate Views | 2024 | SIGMOD | 4.8114797e-05 |
| 2,402 | Causality and Explanations in Databases | 2014 | VLDB | 8.8928361e-05 |
| 4,658 | ExplainIt! - A Declarative Root-cause Analysis Engine for Time Series Data | 2019 | SIGMOD | 6.0183783e-05 |
| 10,428 | CausalExplain: Causal Explanations of Black-box Models with Training Data Subsets | 2025 | SIGMOD | 4.1945683e-05 |
| 8,996 | MetaInsight: Automatic Discovery of Structured Knowledge for Exploratory Data Analysis | 2021 | SIGMOD | 4.4124959e-05 |
| 6,565 | Toward Interpretable and Actionable Data Analysis with Explanations and Causality | 2022 | VLDB | 5.0081626e-05 |