# Matching Algorithms for Blood Donation

arXiv: https://arxiv.org/abs/2108.04862 · HTML: https://mlsanity.com/paper/2108.04862/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 67 references that are in the map.
- First posted on arXiv 10 Aug 2021.

## Builds on (most cited first, 5 of 5)

- [Fair Dynamic Rationing](https://mlsanity.com/paper/2102.01240/) (2021)
- [Allocation Problems in Ride-Sharing Platforms: Online Matching with Offline Reusable Resources](https://mlsanity.com/paper/1711.08345/) (2017)
- [Online Policies for Efficient Volunteer Crowdsourcing](https://mlsanity.com/paper/2002.08474/) (2020)
- [Kidney Exchange in Dynamic Sparse Heterogenous Pools](https://mlsanity.com/paper/1301.3509/) (2013)
- [Matching with our Eyes Closed](https://mlsanity.com/paper/1306.2988/) (2013)

## Built on by (most cited first, 7 of 7)

- [Towards Human-AI Complementarity in Matching Tasks](https://mlsanity.com/paper/2508.13285/) (2025)
- [Fairness Maximization among Offline Agents in Online-Matching Markets](https://mlsanity.com/paper/2109.08934/) (2021)
- [Global Rewards in Restless Multi-Armed Bandits](https://mlsanity.com/paper/2406.00738/) (2024)
- [Non-Stationary Latent Auto-Regressive Bandits](https://mlsanity.com/paper/2402.03110/) (2024)
- [Matchings, Predictions and Counterfactual Harm in Refugee Resettlement Processes](https://mlsanity.com/paper/2407.13052/) (2024)
- [Optimal Efficiency-Envy Trade-Off via Optimal Transport](https://mlsanity.com/paper/2209.15416/) (2022)
- [Optimizing Donor Outreach for Blood Collection Sessions: A Scalable Decision Support Framework](https://mlsanity.com/paper/2603.29643/) (2026)

## Abstract (arXiv)

Global demand for donated blood far exceeds supply, and unmet need is greatest in low- and middle-income countries; experts suggest that large-scale coordination is necessary to alleviate demand. Using the Facebook Blood Donation tool, we conduct the first large-scale algorithmic matching of blood donors with donation opportunities. While measuring actual donation rates remains a challenge, we measure donor action (e.g., making a donation appointment) as a proxy for actual donation. We develop automated policies for matching donors with donation opportunities, based on an online matching model. We provide theoretical guarantees for these policies, both regarding the number of expected donations and the equitable treatment of blood recipients. In simulations, a simple matching strategy increases the number of donations by 5-10%; a pilot experiment with real donors shows a 5% relative increase in donor action rate (from 3.7% to 3.9%). When scaled to the global Blood Donation tool user base, this corresponds to an increase of around one hundred thousand users taking action toward donation. Further, observing donor action on a social network can shed light onto donor behavior and response to incentives. Our initial findings align with several observations made in the medical and social science literature regarding donor behavior.