Summary of Learning Treatment Effects While Treating Those in Need, by Bryan Wilder et al.
Learning treatment effects while treating those in need
by Bryan Wilder, Pim Welle
First submitted to arxiv on: 10 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Methodology (stat.ME); Machine Learning (stat.ML)
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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 This paper presents a framework for designing randomized allocation rules that balance the goals of targeting high-need individuals and evaluating the causal effect of social programs. The proposed approach optimizes policy learning, providing sample complexity guarantees and computationally efficient strategies. Applying this framework to human services data from Allegheny County, Pennsylvania, demonstrates substantial mitigation of the tradeoff between learning and targeting. For instance, it is possible to achieve 90% of optimal utility in targeting high-need individuals while estimating average treatment effects with less than twice the samples required by a randomized controlled trial. The paper highlights the potential impact of algorithmic systems in public services when incorporating program evaluation as an explicit goal alongside targeting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to help people who really need it, but you want to know if your efforts are working too. This is a problem many social programs face. The paper suggests a way to allocate resources that balances helping those in most need with figuring out what’s actually making a difference. They tested this approach on real data from a county in the United States and found that it can make a big difference. For example, you could help 90% of people who really need it while still learning what works best. The paper shows that by combining these two goals, you can create more effective programs that truly make a positive impact. |
