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Consistent Range Approximation for Fair Predictive Modeling

Summary: Frames fairness certification as consistent range approximation (CRA) of fairness queries, leveraging query-answering methods for incomplete/inconsistent databases plus explicit data-collection bias models. CRA derives provable target-population ranges from biased data (optionally using limited population stats) to train models certifiably fair without external data, empirically improving over prior methods. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
13134
Venue
VLDB
Year
2023
Pagerank
4.6353072e-05
Overall Rank
7,851 | 45.39%
DOI
10.14778/3611479.3611498

Incoming Non-self Citations Over Time

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Incoming Citations (Sorted by Pagerank)

Showing 6 of 6 citing papers.

Rank Citing Paper Year Venue Pagerank
9,871 From Logs to Causal Inference: Diagnosing Large Systems 2025 VLDB 4.2667743e-05
9,928 Fainder: A Fast and Accurate Index for Distribution-Aware Dataset Search 2024 VLDB 4.2511622e-05
10,213 Stress-Testing Causal Claims via Cardinality Repairs 2026 SIGMOD 4.1945683e-05
10,223 On Fair Epsilon Net and Geometric Hitting Set 2026 VLDB 4.1945683e-05
10,341 A Theoretical Framework for Distribution-Aware Dataset Search 2025 PODS 4.1945683e-05
10,581 Causal DAG Summarization 2025 VLDB 4.1945683e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 7 of 7 cited papers.

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

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