# The Value of Prediction in Identifying the Worst-Off

arXiv: https://arxiv.org/abs/2501.19334 · HTML: https://mlsanity.com/paper/2501.19334/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 36 references that are in the map.
- First posted on arXiv 31 Jan 2025.

## Builds on (most cited first, 11 of 11)

- [Smart "Predict, then Optimize"](https://mlsanity.com/paper/1710.08005/) (2017)
- [Difficult Lessons on Social Prediction from Wisconsin Public Schools](https://mlsanity.com/paper/2304.06205/) (2023)
- [A Validity Perspective on Evaluating the Justified Use of Data-driven Decision-making Algorithms](https://mlsanity.com/paper/2206.14983/) (2022)
- [Causal Decision Making and Causal Effect Estimation Are Not the Same... and Why It Matters](https://mlsanity.com/paper/2104.04103/) (2021)
- [More Efficient Policy Learning via Optimal Retargeting](https://mlsanity.com/paper/1906.08611/) (2019)
- [The Relative Value of Prediction in Algorithmic Decision Making](https://mlsanity.com/paper/2312.08511/) (2023)
- [Counterfactual Prediction Under Outcome Measurement Error](https://mlsanity.com/paper/2302.11121/) (2023)
- [Allocation Requires Prediction Only if Inequality Is Low](https://mlsanity.com/paper/2406.13882/) (2024)
- [Learning treatment effects while treating those in need](https://mlsanity.com/paper/2407.07596/) (2024)
- [Scarce Resource Allocations That Rely On Machine Learning Should Be Randomized](https://mlsanity.com/paper/2404.08592/) (2024)
- [Evaluating the Effectiveness of Index-Based Treatment Allocation](https://mlsanity.com/paper/2402.11771/) (2024)

## Built on by (most cited first, 5 of 5)

- [MLReplicate: Benchmarking Autonomous Research Systems for Machine Learning Reproducibility](https://mlsanity.com/paper/2605.16616/) (2026)
- [Good Allocations from Bad Estimates](https://mlsanity.com/paper/2601.05597/) (2026)
- [Performative Learning Theory](https://mlsanity.com/paper/2602.04402/) (2026)
- [On the Meta-Design of Allocation Problems](https://mlsanity.com/paper/2602.08786/) (2026)
- [The Limits of AI-Driven Allocation: Optimal Screening under Aleatoric Uncertainty](https://mlsanity.com/paper/2605.07979/) (2026)

## Abstract (arXiv)

Machine learning is increasingly used in government programs to identify and support the most vulnerable individuals, prioritizing assistance for those at greatest risk over optimizing aggregate outcomes. This paper examines the welfare impacts of prediction in equity-driven contexts, and how they compare to other policy levers, such as expanding bureaucratic capacity. Through mathematical models and a real-world case study on long-term unemployment amongst German residents, we develop a comprehensive understanding of the relative effectiveness of prediction in surfacing the worst-off. Our findings provide clear analytical frameworks and practical, data-driven tools that empower policymakers to make principled decisions when designing these systems.