Efficient Public Health Intervention Planning Using Decomposition-Based Decision-Focused Learning
This work solves a computational bottleneck for public health organizations, enabling scalable deployment of intervention planning to potentially millions of beneficiaries, though it is incremental in improving existing DFL methods.
The paper tackled the problem of efficiently optimizing limited health worker interventions to retain beneficiaries in public health programs by addressing the high computational cost of Decision-Focused Learning (DFL) for Restless Multi-Armed Bandits (RMABs). The result was an approach that is up to two orders of magnitude faster than the state-of-the-art while yielding superior model performance, demonstrated using real-world data from an Indian NGO.
The declining participation of beneficiaries over time is a key concern in public health programs. A popular strategy for improving retention is to have health workers `intervene' on beneficiaries at risk of dropping out. However, the availability and time of these health workers are limited resources. As a result, there has been a line of research on optimizing these limited intervention resources using Restless Multi-Armed Bandits (RMABs). The key technical barrier to using this framework in practice lies in the need to estimate the beneficiaries' RMAB parameters from historical data. Recent research has shown that Decision-Focused Learning (DFL), which focuses on maximizing the beneficiaries' adherence rather than predictive accuracy, improves the performance of intervention targeting using RMABs. Unfortunately, these gains come at a high computational cost because of the need to solve and evaluate the RMAB in each DFL training step. In this paper, we provide a principled way to exploit the structure of RMABs to speed up intervention planning by cleverly decoupling the planning for different beneficiaries. We use real-world data from an Indian NGO, ARMMAN, to show that our approach is up to two orders of magnitude faster than the state-of-the-art approach while also yielding superior model performance. This would enable the NGO to scale up deployments using DFL to potentially millions of mothers, ultimately advancing progress toward UNSDG 3.1.