mlsanityData to 7 Oct 2026

Evaluating Treatment Prioritization Rules via Rank-Weighted Average Treatment Effects

arXiv 2111.07966 ↗First posted 15 Nov 2021stat.ME stat.MLcited by 29

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Abstract (arXiv)

There are a number of available methods for selecting whom to prioritize for treatment, including ones based on treatment effect estimation, risk scoring, and hand-crafted rules. We propose rank-weighted average treatment effect (RATE) metrics as a simple and general family of metrics for comparing and testing the quality of treatment prioritization rules. RATE metrics are agnostic as to how the prioritization rules were derived, and only assess how well they identify individuals that benefit the most from treatment. We define a family of RATE estimators and prove a central limit theorem that enables asymptotically exact inference in a wide variety of randomized and observational study settings. RATE metrics subsume a number of existing metrics, including the Qini coefficient, and our analysis directly yields inference methods for these metrics. We showcase RATE in the context of a number of applications, including optimal targeting of aspirin to stroke patients.

Key facts. Cited by 29 papers in the map (data through 7 Oct 2026). Builds on 8 of its 92 references that are in the map. First posted on arXiv 15 Nov 2021.

Builds on

8 of 92 references.

References · 8

  1. 2015Estimation and Inference of Heterogeneous Treatment Effects using Random Forestscited by 597
  2. 2017Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learningcited by 475
  3. 2017Quasi-Oracle Estimation of Heterogeneous Treatment Effectscited by 328
  4. 2016Generalized Random Forestscited by 309
  5. 2019Machine Learning Methods Economists Should Know Aboutcited by 63
  6. 2020Estimating heterogeneous treatment effects with right-censored data via causal survival forestscited by 29
  7. 2019Experimental Evaluation of Individualized Treatment Rulescited by 16
  8. 2021Treatment Allocation under Uncertain Costscited by 9

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Citing papers per year: 2022: 5; 2023: 6; 2024: 5; 2025: 7; 2026: 6.

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  1. 2023Diagnosing Model Performance Under Distribution Shiftcited by 37
  2. 2023Zero-shot causal learningcited by 17
  3. 2023Causal isotonic calibration for heterogeneous treatment effectscited by 16
  4. 2023Machine Learning Who to Nudge: Causal vs Predictive Targeting in a Field Experiment on Student Financial Aid Renewalcited by 8
  5. 2022Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experimentscited by 7
  6. 2022Robust Design and Evaluation of Predictive Algorithms under Unobserved Confoundingcited by 7
  7. 2023Proximal Causal Learning of Conditional Average Treatment Effectscited by 7
  8. 2022Calibration Error for Heterogeneous Treatment Effectscited by 5
  9. 2024Evaluating the Effectiveness of Index-Based Treatment Allocationcited by 5
  10. 2022Heterogeneous Treatment Effect with Trained Kernels of the Nadaraya-Watson Regressioncited by 4
  11. 2024Optimizing Multi-Scale Representations to Detect Effect Heterogeneity Using Earth Observation and Computer Vision: Applications to Two Anti-Poverty RCTscited by 4
  12. 2024Comparing Targeting Strategies for Maximizing Social Welfare with Limited Resourcescited by 4
17 more
  1. 2025CTRL Your Shift: Clustered Transfer Residual Learning for Many Small Datasetscited by 2
  2. 2025Federated Learning of Quantile Inference under Local Differential Privacycited by 2
  3. 2025Fostering the Ecosystem of AI for Social Impact Requires Expanding and Strengthening Evaluation Standardscited by 2
  4. 2026Rank-Learner: Orthogonal Ranking of Treatment Effectscited by 2
  5. 2026A Statistical Test for the Benefits of Personalizing Interventionscited by 2
  6. 2026UpliftBench: Revealing Outcome-Regime and Objective Mismatch in Uplift Evaluationcited by 1
  7. 2022Improving uplift model evaluation on RCT datacited by 0
  8. 2023Statistical Performance Guarantee for Subgroup Identification with Generic Machine Learningcited by 0
  9. 2024Neyman Meets Causal Machine Learning: Experimental Evaluation of Individualized Treatment Rulescited by 0
  10. 2024Causal Post-Processing of Predictive Modelscited by 0
  11. 2025Qini Curve Estimation under Clustered Network Interferencecited by 0
  12. 2025Statistical Learning for Heterogeneous Treatment Effects: Pretraining, Prognosis, and Predictioncited by 0
  13. 2025Annotation-Assisted Learning of Treatment Policies From Multimodal Electronic Health Recordscited by 0
  14. 2025Learning to Optimize Feedback for One Million Students: Insights from Multi-Armed and Contextual Bandits in Large-Scale Online Tutoringcited by 0
  15. 2026Real vs. Semi-Simulated: Rethinking Evaluation for Treatment Effect Estimationcited by 0
  16. 2026Detecting an Effect Is Not Learning to Act on It: A Reward-SNR Floor for LLM Acquisition Agentscited by 0
  17. 2026A Multimodal Foundation Model for Longitudinal Patient Representation and Scalable Insight Generation in Oncologycited by 0