# A Graph-Prediction-Based Approach for Debiasing Underreported Data

arXiv: https://arxiv.org/abs/2307.07898 · HTML: https://mlsanity.com/paper/2307.07898/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jul 2023.

## Built on by (most cited first, 3 of 3)

- [Black-Box Optimization with Implicit Constraints for Public Policy](https://mlsanity.com/paper/2310.18449/) (2023)
- [Improving the Fairness of Deep-Learning, Short-term Crime Prediction with Under-reporting-aware Models](https://mlsanity.com/paper/2406.04382/) (2024)
- [Graph-Based Prediction Models for Data Debiasing](https://mlsanity.com/paper/2504.09348/) (2025)

## Abstract (arXiv)

We present a novel Graph-based debiasing Algorithm for Underreported Data (GRAUD) aiming at an efficient joint estimation of event counts and discovery probabilities across spatial or graphical structures. This innovative method provides a solution to problems seen in fields such as policing data and COVID-19 data analysis. Our approach avoids the need for strong priors typically associated with Bayesian frameworks. By leveraging the graph structures on unknown variables n and p, our method debiases the under-report data and estimates the discovery probability at the same time. We validate the effectiveness of our method through simulation experiments and illustrate its practicality in one real-world application: police 911 calls-to-service data.