Demonstration of Inferring Causality from Relational Databases with CaRL
Summary: CaRL is an end-to-end system for causal inference over relational databases, removing the homogeneous-unit/flat-table assumption of conventional observational methods. A visual interface enables live investigations on academic and medical data. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
- 1. Moe Kayali (University of Washington)
- 2. Babak Salimi (University of Washington)
- 3. Dan Suciu (University of Washington)
BibTeX Citation
@article{kayali_vldb20,
title = {{Demonstration of Inferring Causality from Relational Databases with CaRL}},
author = {Kayali, Moe and Salimi, Babak and Suciu, Dan},
journal = {PVLDB},
series = {{VLDB} '20},
volume = {13},
number = {12},
pages = {2985--2988},
doi = {10.14778/3415478.3415525},
url = {https://doi.org/10.14778/3415478.3415525},
year = {2020}
}Incoming Citations (Sorted by Pagerank)
Showing 0 of 0 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 1 of 1 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,374 | Causal Relational Learning | 2020 | SIGMOD | 8.6755064e-05 |
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 4,548 | ExplainIt! - A Declarative Root-cause Analysis Engine for Time Series Data | 2019 | SIGMOD |
| 2 | 10,018 | From Logs to Causal Inference: Diagnosing Large Systems | 2025 | VLDB |
| 3 | 6,054 | Causal Data Integration | 2023 | VLDB |
| 4 | 14,461 | Relation Data Model with Cause-Effect Association | 1986 | VLDB |
| 5 | 10,708 | CausaLens: A System for Summarizing Causal DAGs | 2025 | SIGMOD |
| 6 | 6,576 | Toward Interpretable and Actionable Data Analysis with Explanations and Causality | 2022 | VLDB |
| 7 | 1,943 | Causality and Explanations in Databases | 2014 | VLDB |
| 8 | 10,958 | What If: Causal Analysis with Graph Databases | 2025 | VLDB |
| 9 | 8,415 | ZaliQL: Causal Inference from Observational Data at Scale | 2017 | VLDB |
| 10 | 2,374 | Causal Relational Learning | 2020 | SIGMOD |