# Metalearners for Ranking Treatment Effects

arXiv: https://arxiv.org/abs/2405.02183 · HTML: https://mlsanity.com/paper/2405.02183/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 65 references that are in the map.
- First posted on arXiv 3 May 2024.

## Builds on (most cited first, 8 of 8)

- [Decision-Focused Learning: Foundations, State of the Art, Benchmark and Future Opportunities](https://mlsanity.com/paper/2307.13565/) (2023)
- [An End-to-End Framework for Marketing Effectiveness Optimization under Budget Constraint](https://mlsanity.com/paper/2302.04477/) (2023)
- [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)
- [The foundations of cost-sensitive causal classification](https://mlsanity.com/paper/2007.12582/) (2020)
- [Heterogeneous Causal Learning for Effectiveness Optimization in User Marketing](https://mlsanity.com/paper/2004.09702/) (2020)
- [Qini Curves for Multi-Armed Treatment Rules](https://mlsanity.com/paper/2306.11979/) (2023)
- [Incremental Profit per Conversion: a Response Transformation for Uplift Modeling in E-Commerce Promotions](https://mlsanity.com/paper/2306.13759/) (2023)
- [Causal Ordering Without Effect Estimation: A Framework for Using Proxies in Treatment Prioritization](https://mlsanity.com/paper/2206.12532/) (2022)

## Built on by (most cited first, 3 of 3)

- [Aligning Data-Driven Predictors with Allocation: A Decision-Focused Approach to Survival Analysis](https://mlsanity.com/paper/2606.02671/) (2026)
- [Hidden Representation Clustering with Multi-Task Representation Learning towards Robust Online Budget Allocation](https://mlsanity.com/paper/2506.00959/) (2025)
- [Learning Who Benefits Most: Multi-Treatment Causal Ranking for Population Health Stratification](https://mlsanity.com/paper/2610.19782/) (2026)

## Abstract (arXiv)

Efficiently allocating treatments with a budget constraint constitutes an important challenge across various domains. In marketing, for example, the use of promotions to target potential customers and boost conversions is limited by the available budget. While much research focuses on estimating causal effects, there is relatively limited work on learning to allocate treatments while considering the operational context. Existing methods for uplift modeling or causal inference primarily estimate treatment effects, without considering how this relates to a profit maximizing allocation policy that respects budget constraints. The potential downside of using these methods is that the resulting predictive model is not aligned with the operational context. Therefore, prediction errors are propagated to the optimization of the budget allocation problem, subsequently leading to a suboptimal allocation policy. We propose an alternative approach based on learning to rank. Our proposed methodology directly learns an allocation policy by prioritizing instances in terms of their incremental profit. We propose an efficient sampling procedure for the optimization of the ranking model to scale our methodology to large-scale data sets. Theoretically, we show how learning to rank can maximize the area under a policy's incremental profit curve. Empirically, we validate our methodology and show its effectiveness in practice through a series of experiments on both synthetic and real-world data.