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 (Technion)
- 2. Anna Zeng (Massachusetts Institute of Technology)
- 3. Michael John Cafarella (Massachusetts Institute of Technology)
- 4. Batya Kenig (Technion)
- 5. Markos Markakis (Massachusetts Institute of Technology)
- 6. Oren Mishali (Technion)
- 7. Brit Youngmann (Technion)
- 8. Babak Salimi (University of California San Diego)
BibTeX Citation
@inproceedings{chen_sigmod25,
title = {{CausaLens: A System for Summarizing Causal DAGs}},
author = {Chen, Noam and Zeng, Anna and Cafarella, Michael John and Kenig, Batya and Markakis, Markos and Mishali, Oren and Youngmann, Brit and Salimi, Babak},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3722212.3725086},
url = {https://dl.acm.org/doi/10.1145/3722212.3725086},
year = {2025}
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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 |
|---|---|---|---|---|
| 486 | Efficient Aggregation for Graph Summarization | 2008 | SIGMOD | 0.00017692185 |
| 863 | Interventional Fairness : Causal Database Repair for Algorithmic Fairness | 2019 | SIGMOD | 0.00013531835 |
| 4,953 | Efficient Graph Summarization using Weighted LSH at Billion-Scale | 2021 | SIGMOD | 6.4303691e-05 |
| 6,932 | Summarized Causal Explanations For Aggregate Views | 2024 | SIGMOD | 5.7362362e-05 |
| 9,055 | Causal DAG Summarization | 2025 | VLDB | 5.3251649e-05 |
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