# Comparing Targeting Strategies for Maximizing Social Welfare with Limited Resources

arXiv: https://arxiv.org/abs/2411.07414 · HTML: https://mlsanity.com/paper/2411.07414/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 41 references that are in the map.
- First posted on arXiv 11 Nov 2024.

## Builds on (most cited first, 9 of 9)

- [Estimation and Inference of Heterogeneous Treatment Effects using Random Forests](https://mlsanity.com/paper/1510.04342/) (2015)
- [Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning](https://mlsanity.com/paper/1706.03461/) (2017)
- [Semiparametric doubly robust targeted double machine learning: a review](https://mlsanity.com/paper/2203.06469/) (2022)
- [Difficult Lessons on Social Prediction from Wisconsin Public Schools](https://mlsanity.com/paper/2304.06205/) (2023)
- [Evaluating Treatment Prioritization Rules via Rank-Weighted Average Treatment Effects](https://mlsanity.com/paper/2111.07966/) (2021)
- [Machine Learning Who to Nudge: Causal vs Predictive Targeting in a Field Experiment on Student Financial Aid Renewal](https://mlsanity.com/paper/2310.08672/) (2023)
- [Learning treatment effects while treating those in need](https://mlsanity.com/paper/2407.07596/) (2024)
- [Estimating scaled treatment effects with multiple outcomes](https://mlsanity.com/paper/1608.02273/) (2016)
- [Causal Inference Struggles with Agency on Online Platforms](https://mlsanity.com/paper/2107.08995/) (2021)

## Built on by (most cited first, 4 of 4)

- [Good Allocations from Bad Estimates](https://mlsanity.com/paper/2601.05597/) (2026)
- [Reconsidering Fairness Through Unawareness From the Perspective of Model Multiplicity](https://mlsanity.com/paper/2505.16638/) (2025)
- [On the Meta-Design of Allocation Problems](https://mlsanity.com/paper/2602.08786/) (2026)
- [Learning Who to Treat When Treatment is Missing](https://mlsanity.com/paper/2607.14346/) (2026)

## Abstract (arXiv)

Machine learning is increasingly used to select which individuals receive limited-resource interventions in domains such as human services, education, development, and more. However, it is often not apparent what the right quantity is for models to predict. Policymakers rarely have access to data from a randomized controlled trial (RCT) that would enable accurate estimates of which individuals would benefit more from the intervention, while observational data creates a substantial risk of bias in treatment effect estimates. Practitioners instead commonly use a technique termed ``risk-based targeting" where the model is just used to predict each individual's status quo outcome (an easier, non-causal task). Those with higher predicted risk are offered treatment. There is currently almost no empirical evidence to inform which choices lead to the most effective machine learning-informed targeting strategies in social domains. In this work, we use data from 5 real-world RCTs in a variety of domains to empirically assess such choices. We find that when treatment effects can be estimated with high accuracy (which we simulate by allowing the model to partially observe outcomes in advance), treatment effect based targeting substantially outperforms risk-based targeting, even when treatment effect estimates are biased. Moreover, these results hold even when the policymaker has strong normative preferences for assisting higher-risk individuals. However, the features and data actually available in most RCTs we examine do not suffice for accurate estimates of heterogeneous treatment effects. Our results suggest treatment effect targeting has significant potential benefits, but realizing these benefits requires improvements to data collection and model training beyond what is currently common in practice.