  We developed a common algorithmic solution addressing the problem of
resource-constrained outreach encountered by social change organizations with
different missions and operations: Breaking Ground -- an organization that
helps individuals experiencing homelessness in New York transition to permanent
housing and Leket -- the national food bank of Israel that rescues food from
farms and elsewhere to feed the hungry. Specifically, we developed an
estimation and optimization approach for partially-observed episodic restless
bandits under $k$-step transitions. The results show that our Thompson sampling
with Markov chain recovery (via Stein variational gradient descent) algorithm
significantly outperforms baselines for the problems of both organizations. We
carried out this work in a prospective manner with the express goal of devising
a flexible-enough but also useful-enough solution that can help overcome a lack
of sustainable impact in data science for social good.
