Graph-Based Prediction Models for Data Debiasing

• URL: http://arxiv.org/abs/2504.09348v2
• Date: Sat, 19 Apr 2025 03:17:38 GMT
• Title: Graph-Based Prediction Models for Data Debiasing
• Authors: Dongze Wu, Hanyang Jiang, Yao Xie,
• Abstract summary: Bias in data collection, arising from both under-reporting and over-reporting, poses significant challenges in healthcare and public safety.<n>We introduce Graph-based Over- and Under-reporting Debiasing (GROUD), a novel graph-based optimization framework that debiases reported data by jointly estimating the true incident counts and the associated reporting bias probabilities.<n>We validate GROUD on both challenging simulated experiments and real-world datasets, including Atlanta emergency calls and COVID-19 vaccine adverse event reports.
• Score: 6.221408085892461
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Bias in data collection, arising from both under-reporting and over-reporting, poses significant challenges in critical applications such as healthcare and public safety. In this work, we introduce Graph-based Over- and Under-reporting Debiasing (GROUD), a novel graph-based optimization framework that debiases reported data by jointly estimating the true incident counts and the associated reporting bias probabilities. By modeling the bias as a smooth signal over a graph constructed from geophysical or feature-based similarities, our convex formulation not only ensures a unique solution but also comes with theoretical recovery guarantees under certain assumptions. We validate GROUD on both challenging simulated experiments and real-world datasets -- including Atlanta emergency calls and COVID-19 vaccine adverse event reports -- demonstrating its robustness and superior performance in accurately recovering debiased counts. This approach paves the way for more reliable downstream decision-making in systems affected by reporting irregularities.

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