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Causal Data Integration

Summary: Defines Causal Data Integration (CDI): mining missing attributes from external sources and auto-building causal DAGs to enable causal inference over partial datasets. Gives a system architecture, key challenges and algorithms, and preliminary experiments demonstrating feasibility for recovering missing covariates and correcting mis-specified variable selection. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
h547b145d31c1e0ee
Venue
VLDB
Year
2023
Pagerank
5.8559807e-05
Overall Rank
6,186 | 58.43%
DOI
10.14778/3603581.3603602
PDF
Download (CC BY-NC-ND 4.0)

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{youngmann_vldb23,
        title = {{Causal Data Integration}},
        author = {Youngmann, Brit and Cafarella, Michael and Salimi, Babak and Zeng, Anna},
        journal = {PVLDB},
        series = {{VLDB} '23},
        volume = {16},
        number = {10},
        pages = {2659--2665},
        doi = {10.14778/3603581.3603602},
        url = {https://doi.org/10.14778/3603581.3603602},
        year = {2023}
}

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Showing 12 of 12 cited papers.

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