CausaLens: A System for Summarizing Causal DAGs
Summary: CausaLens summarizes high-dimensional causal DAGs, preserving essential causal information in a compact form. SIGMOD'25 demo shows the summary DAG enables inference robust to DAG misspecification, outperforming full-DAG analysis. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Noam Chen
- 2. Anna Zeng
- 3. Michael John Cafarella
- 4. Batya Kenig
- 5. Markos Markakis
- 6. Oren Mishali
- 7. Brit Youngmann
- 8. Babak Salimi
Incoming Citations (Sorted by Pagerank)
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Outgoing Citations (Sorted by Pagerank)
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 435 | Efficient Aggregation for Graph Summarization | 2008 | SIGMOD | 0.00023260172 |
| 1,041 | Interventional Fairness : Causal Database Repair for Algorithmic Fairness | 2019 | SIGMOD | 0.00014482047 |
| 4,761 | Efficient Graph Summarization using Weighted LSH at Billion-Scale | 2021 | SIGMOD | 5.9404527e-05 |
| 7,172 | Summarized Causal Explanations For Aggregate Views | 2024 | SIGMOD | 4.8114797e-05 |
| 10,581 | Causal DAG Summarization | 2025 | VLDB | 4.1945683e-05 |
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