# On the Meta-Design of Allocation Problems

arXiv: https://arxiv.org/abs/2602.08786 · HTML: https://mlsanity.com/paper/2602.08786/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 58 references that are in the map.
- First posted on arXiv 9 Feb 2026.

## Builds on (most cited first, 20 of 20)

- [Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning](https://mlsanity.com/paper/1706.03461/) (2017)
- [Smart "Predict, then Optimize"](https://mlsanity.com/paper/1710.08005/) (2017)
- [Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goals](https://mlsanity.com/paper/1809.04198/) (2018)
- [Counterfactual Risk Assessments, Evaluation, and Fairness](https://mlsanity.com/paper/1909.00066/) (2019)
- [Balanced Policy Evaluation and Learning](https://mlsanity.com/paper/1705.07384/) (2017)
- [Difficult Lessons on Social Prediction from Wisconsin Public Schools](https://mlsanity.com/paper/2304.06205/) (2023)
- [Contextual Bandits with Packing and Covering Constraints: A Modular Lagrangian Approach via Regression](https://mlsanity.com/paper/2211.07484/) (2022)
- [Omnipredictors for Constrained Optimization](https://mlsanity.com/paper/2209.07463/) (2022)
- [The Relative Value of Prediction in Algorithmic Decision Making](https://mlsanity.com/paper/2312.08511/) (2023)
- [From the Fair Distribution of Predictions to the Fair Distribution of Social Goods: Evaluating the Impact of Fair Machine Learning on Long-Term Unemployment](https://mlsanity.com/paper/2401.14438/) (2024)
- [Allocation Requires Prediction Only if Inequality Is Low](https://mlsanity.com/paper/2406.13882/) (2024)
- [Scarce Resource Allocations That Rely On Machine Learning Should Be Randomized](https://mlsanity.com/paper/2404.08592/) (2024)
- [Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium](https://mlsanity.com/paper/1912.12864/) (2019)
- [Robust Optimization with Decision-Dependent Information Discovery](https://mlsanity.com/paper/2004.08490/) (2020)
- [The Value of Prediction in Identifying the Worst-Off](https://mlsanity.com/paper/2501.19334/) (2025)
- [Decision-Focused Evaluation of Worst-Case Distribution Shift](https://mlsanity.com/paper/2407.03557/) (2024)
- [Comparing Targeting Strategies for Maximizing Social Welfare with Limited Resources](https://mlsanity.com/paper/2411.07414/) (2024)
- [Bridging Prediction and Intervention Problems in Social Systems](https://mlsanity.com/paper/2507.05216/) (2025)
- [Who Pays the RENT? Implications of Spatial Inequality for Prediction-Based Allocation Policies](https://mlsanity.com/paper/2508.08573/) (2025)
- [Good Allocations from Bad Estimates](https://mlsanity.com/paper/2601.05597/) (2026)

## Abstract (arXiv)

There is an extensive literature that studies how to find optimal policies in resource allocation problems, taking the underlying design parameters that define the allocation, such as what data is collected, how many people can be served, and quality of service as fixed constraints. Yet, from a planner's perspective, these design parameters are themselves optimization variables that are just as important in determining overall welfare as selecting the optimal targeting rule for a given set of constraints. This realization motivates a rich set of meta-design questions exploring how planners should make principled decisions about investments in prediction, capacity constraints, and treatment quality, all of which lie upstream of classical policy optimization. Building on initial theoretical work in this space, our paper has three main contributions. First, we formally define the broad meta-design space of resource allocation problems. Second, we develop empirical tools that enable practitioners to reliably navigate it. Third, we demonstrate the framework in two real-world case studies on German employment services and targeted cash transfer programs in Ethiopia.