  Applications of machine learning in the non-profit and public sectors often
feature an iterative workflow of data acquisition, prediction, and optimization
of interventions. There are four major pain points that a machine learning
pipeline must overcome in order to be actually useful in these settings: small
data, data collected only under the default intervention, unmodeled objectives
due to communication gap, and unforeseen consequences of the intervention. In
this paper, we introduce bandit data-driven optimization, the first iterative
prediction-prescription framework to address these pain points. Bandit
data-driven optimization combines the advantages of online bandit learning and
offline predictive analytics in an integrated framework. We propose PROOF, a
novel algorithm for this framework and formally prove that it has no-regret.
Using numerical simulations, we show that PROOF achieves superior performance
than existing baseline. We also apply PROOF in a detailed case study of food
rescue volunteer recommendation, and show that PROOF as a framework works well
with the intricacies of ML models in real-world AI for non-profit and public
sector applications.
