Rahul Ladhania, Jann Spiess, Lyle Ungar, Wenbo Wu
arXiv 1 Nov 2023 · Statistics — Machine Learning · 1 citations (OpenAlex)
arXiv:2311.00577 · PDF · DOI · OpenAlex · Extracted main text
We consider learning personalized assignments to one of many treatment arms from a randomized controlled trial. Standard methods that estimate heterogeneous treatment effects separately for each arm may perform poorly in this case due to excess variance. We instead propose methods that pool information across treatment arms: First, we consider a regularized forest-based assignment algorithm based on greedy recursive partitioning that shrinks effect estimates across arms. Second, we augment our algorithm by a clustering scheme that combines treatment arms with consistently similar outcomes. In a simulation study, we compare the performance of these approaches to predicting arm-wise outcomes separately, and document gains of directly optimizing the treatment assignment with regularization and clustering. In a theoretical model, we illustrate how a high number of treatment arms makes finding the best arm hard, while we can achieve sizable utility gains from personalization by regularized optimization.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Athey, Susan, Julie Tibshirani, and Stefan Wager (2019) Generalized random forests | 1.000 | 5 | 3 | 100% |
| 2 | Kallus, Nathan (2017) Recursive Partitioning for Personalization using Observational Data | 0.874 | 5 | 2 | 100% |
| 3 | Ma, Haixu, Donglin Zeng, and Yufeng Liu (2022) Learning individualized treatment rules with many treatments: A supervised clustering approach using adaptive fusion | 0.811 | 4 | 2 | 100% |
| 4 | Wu, Edward and Johann A Gagnon-Bartsch (2018) The LOOP Estimator: Adjusting for Covariates in Randomized Experiments | 0.644 | 3 | 2 | 67% |
| 5 | Hitsch, Ggnter J and Sanjog Misra (2018) Heterogeneous treatment effects and optimal targeting policy evaluation | 0.644 | 2 | 2 | 100% |
| 6 | Nie, Xinkun and Stefan Wager (2021) Quasi-oracle estimation of heterogeneous treatment effects | 0.644 | 2 | 2 | 100% |
| 7 | Spiess, Jann (2018) Optimal Estimation when Researcher and Social Preferences are Misaligned self | 0.511 | 2 | 2 | 50% |
| 8 | Kitagawa, Toru and Aleksey Tetenov (2018) Who should be treated? empirical welfare maximization methods for treatment choice | 0.511 | 2 | 1 | 100% |
| 9 | Sverdrup, Erik, Ayush Kanodia, Zhengyuan Zhou, Susan Athey, and Stef… (2020) policytree: Policy learning via doubly robust empirical welfare maximization over trees | 0.511 | 2 | 1 | 100% |
| 10 | Athey, Susan and Guido Imbens (2016) Recursive partitioning for heterogeneous causal effects | 0.511 | 2 | 1 | 100% |
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