Causal DAG Summarization
Summary: Proposes a causal DAG summarization objective that compresses large DAGs while preserving identification-relevant structure for valid adjustment. Gives an efficient greedy algorithm producing summary DAGs usable directly for inference and empirically more robust to misspecification than prior methods. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Anna Zeng (Massachusetts Institute of Technology)
- 2. Michael Cafarella (Massachusetts Institute of Technology)
- 3. Batya Kenig (Technion)
- 4. Markos Markakis (Massachusetts Institute of Technology)
- 5. Brit Youngmann (Technion)
- 6. Babak Salimi (University of California San Diego)
BibTeX Citation
@article{zeng_vldb25,
title = {{Causal DAG Summarization}},
author = {Zeng, Anna and Cafarella, Michael and Kenig, Batya and Markakis, Markos and Youngmann, Brit and Salimi, Babak},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {6},
pages = {1933--1947},
doi = {10.14778/3725688.3725717},
url = {https://doi.org/10.14778/3725688.3725717},
year = {2025}
}
Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,239 | Fair Data Pre-Processing with Imperfect Attribute Space | 2026 | SIGMOD | 5.093636e-05 |
| 10,436 | Causal Explanations for Disparate Trends: Where and Why? | 2026 | SIGMOD | 5.093636e-05 |
| 10,708 | CausaLens: A System for Summarizing Causal DAGs | 2025 | SIGMOD | 5.093636e-05 |
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 22 of 22 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 10,502 | Stress-Testing Causal Claims via Cardinality Repairs | 2026 | SIGMOD |
| 2 | 1,626 | Scalable Techniques for Mining Causal Structures | 1998 | VLDB |
| 3 | 10,709 | CausalExplain: Causal Explanations of Black-box Models with Training Data Subsets | 2025 | SIGMOD |
| 4 | 10,710 | CauSumX: Summarized Causal Explanations For Group-By-Average Queries | 2025 | SIGMOD |
| 5 | 10,018 | From Logs to Causal Inference: Diagnosing Large Systems | 2025 | VLDB |
| 6 | 2,374 | Causal Relational Learning | 2020 | SIGMOD |
| 7 | 6,054 | Causal Data Integration | 2023 | VLDB |
| 8 | 10,958 | What If: Causal Analysis with Graph Databases | 2025 | VLDB |
| 9 | 6,932 | Summarized Causal Explanations For Aggregate Views | 2024 | SIGMOD |
| 10 | 10,708 | CausaLens: A System for Summarizing Causal DAGs | 2025 | SIGMOD |