  We study the problem of allocating limited supply of medical resources in
developing countries, in particular, Sierra Leone. We address this problem by
combining machine learning (to predict demand) with optimization (to optimize
allocations). A key challenge is the need to align the loss function used to
train the machine learning model with the decision loss associated with the
downstream optimization problem. Traditional solutions have limited flexibility
in the model architecture and scale poorly to large datasets. We propose a
decision-aware learning algorithm that uses a novel Taylor expansion of the
optimal decision loss to derive the machine learning loss. Importantly, our
approach only requires a simple re-weighting of the training data, ensuring it
is both flexible and scalable, e.g., we incorporate it into a random forest
trained using a multitask learning framework. We apply our framework to
optimize the distribution of essential medicines in collaboration with
policymakers in Sierra Leone; highly uncertain demand and limited budgets
currently result in excessive unmet demand. Out-of-sample results demonstrate
that our end-to-end approach can significantly reduce unmet demand across 1040
health facilities throughout Sierra Leone.
