Summary of Efficient Public Health Intervention Planning Using Decomposition-based Decision-focused Learning, by Sanket Shah et al.
Efficient Public Health Intervention Planning Using Decomposition-Based Decision-Focused Learning
by Sanket Shah, Arun Suggala, Milind Tambe, Aparna Taneja
First submitted to arxiv on: 8 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A new approach for optimizing limited intervention resources in public health programs is presented, which addresses the declining participation of beneficiaries over time. The strategy involves using Restless Multi-Armed Bandits (RMABs) to improve retention by intervening on at-risk individuals. However, estimating RMAB parameters from historical data has been a key technical barrier. Decision-Focused Learning (DFL) has shown promise in improving intervention targeting using RMABs, but at a high computational cost. This paper proposes a method for speeding up intervention planning by exploiting the structure of RMABs and decoupling planning for different beneficiaries. The approach is demonstrated to be up to two orders of magnitude faster than state-of-the-art methods while achieving superior model performance on real-world data from ARMMAN, an Indian NGO. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper aims to improve public health programs by optimizing limited intervention resources using Restless Multi-Armed Bandits (RMABs). The main idea is to intervene with people who might drop out of a program. Right now, it’s hard to figure out how to do this because we need historical data and special math tricks. A way called Decision-Focused Learning helps, but it takes too long. This paper shows how to make the process faster while still working well on real-world data. |
