2024

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 estimating the beneficiaries' RMAB parameters from historical data. Recent research on Decision-Focused Learning (DFL) has shown that estimating parameters that maximize beneficiaries' cumulative returns rather than predictive accuracy, is essential to good performance. 

Unfortunately, these gains come at a high computational cost because of the need to solve and evaluate the RMAB in each DFL training step. Consequently, past approaches may not be sustainable for the NGOs that manage such programs in the long run, given that they operate under resource constraints. In this paper, we provide a principled way to exploit the structure of RMABs to speed up DFL by decoupling intervention 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 enables computationally efficient solutions, giving NGOs the ability to deploy such solutions to serve potentially millions of mothers, ultimately advancing progress toward UNSDG 3.1.

Predict-then-Optimize is a framework for using machine learning to perform decision-making under uncertainty. The central research question it asks is, “How can we use the structure of a decision-making task to tailor ML models for that specific task?” To this end, recent work has proposed learning task- specific loss functions that capture this underlying structure. However, current approaches make restrictive assumptions about the form of these losses and their impact on ML model behavior. These assumptions both lead to approaches with high computational cost, and when they are violated in prac- tice, poor performance. In this paper, we propose solutions to these issues, avoiding the aforementioned assumptions and utilizing the ML model’s features to increase the sample effi- ciency of learning loss functions. We empirically show that our method achieves state-of-the-art results in four domains from the literature, often requiring an order of magnitude fewer samples than comparable methods from past work. Moreover, our approach outperforms the best existing method by nearly 200% when the localness assumption is broken.