# Qini Curves for Multi-Armed Treatment Rules

arXiv: https://arxiv.org/abs/2306.11979 · HTML: https://mlsanity.com/paper/2306.11979/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jun 2023.

## Built on by (most cited first, 6 of 6)

- [Optimizing Multi-Scale Representations to Detect Effect Heterogeneity Using Earth Observation and Computer Vision: Applications to Two Anti-Poverty RCTs](https://mlsanity.com/paper/2411.02134/) (2024)
- [Metalearners for Ranking Treatment Effects](https://mlsanity.com/paper/2405.02183/) (2024)
- [Qini Curve Estimation under Clustered Network Interference](https://mlsanity.com/paper/2502.20097/) (2025)
- [Statistical Learning for Heterogeneous Treatment Effects: Pretraining, Prognosis, and Prediction](https://mlsanity.com/paper/2505.00310/) (2025)
- [Deployment of AI-Assisted Interventions: Capacity Constraints and Noisy Compliance](https://mlsanity.com/paper/2604.14370/) (2026)
- [What Would it Cost to End Extreme Poverty?](https://mlsanity.com/paper/2609.02013/) (2026)

## Abstract (arXiv)

Qini curves have emerged as an attractive and popular approach for evaluating the benefit of data-driven targeting rules for treatment allocation. We propose a generalization of the Qini curve to multiple costly treatment arms, that quantifies the value of optimally selecting among both units and treatment arms at different budget levels. We develop an efficient algorithm for computing these curves and propose bootstrap-based confidence intervals that are exact in large samples for any point on the curve. These confidence intervals can be used to conduct hypothesis tests comparing the value of treatment targeting using an optimal combination of arms with using just a subset of arms, or with a non-targeting assignment rule ignoring covariates, at different budget levels. We demonstrate the statistical performance in a simulation experiment and an application to treatment targeting for election turnout.