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From Logs to Causal Inference: Diagnosing Large Systems

Summary: LOGos converts messy system logs into causal-inference tables, with interactive cause ranking and causal-graph refinement. Eliciting only graph structure relevant to a target effect reduces human judgments while enabling unbiased intervention estimates. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
14009
Venue
VLDB
Year
2025
Pagerank
5.1757914e-05
Overall Rank
10,018 | 31.27%
DOI
10.14778/3705829.3705836

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{markakis_vldb25,
        title = {{From Logs to Causal Inference: Diagnosing Large Systems}},
        author = {Markakis, Markos and Youngmann, Brit and Gao, Trinity and Zhang, Ziyu and Shahout, Rana and Chen, Peter Baile and Liu, Chunwei and Sabek, Ibrahim and Cafarella, Michael},
        journal = {PVLDB},
        series = {{VLDB} '25},
        volume = {18},
        number = {2},
        pages = {158--172},
        doi = {10.14778/3705829.3705836},
        url = {https://doi.org/10.14778/3705829.3705836},
        year = {2025}
}

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank Citing Paper Year Venue Pagerank
9,055 Causal DAG Summarization 2025 VLDB 5.3251649e-05
10,502 Stress-Testing Causal Claims via Cardinality Repairs 2026 SIGMOD 5.093636e-05
10,909 LLMLog: Advanced Log Template Generation via LLM-driven Multi-Round Annotation 2025 VLDB 5.093636e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 10 of 10 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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