  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.
