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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2021
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2103.11066 |
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| _version_ | 1866914087941701632 |
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| author | Sun, Hao Munro, Evan Kalashnov, Georgy Du, Shuyang Wager, Stefan |
| author_facet | Sun, Hao Munro, Evan Kalashnov, Georgy Du, Shuyang Wager, Stefan |
| contents | We consider the problem of learning how to optimally allocate treatments whose cost is uncertain and can vary with pre-treatment covariates. This setting may arise in medicine if we need to prioritize access to a scarce resource that different patients would use for different amounts of time, or in marketing if we want to target discounts whose cost to the company depends on how much the discounts are used. Here, we show that the optimal treatment allocation rule under budget constraints is a thresholding rule based on priority scores (those with a higher score are treated first), and we propose a number of practical methods for learning these priority scores using data from a randomized trial. Our formal results leverage a statistical connection between our problem and that of learning heterogeneous treatment effects under endogeneity using an instrumental variable. We find our method to perform well in a number of empirical evaluations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2103_11066 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Treatment Allocation under Uncertain Costs Sun, Hao Munro, Evan Kalashnov, Georgy Du, Shuyang Wager, Stefan Methodology We consider the problem of learning how to optimally allocate treatments whose cost is uncertain and can vary with pre-treatment covariates. This setting may arise in medicine if we need to prioritize access to a scarce resource that different patients would use for different amounts of time, or in marketing if we want to target discounts whose cost to the company depends on how much the discounts are used. Here, we show that the optimal treatment allocation rule under budget constraints is a thresholding rule based on priority scores (those with a higher score are treated first), and we propose a number of practical methods for learning these priority scores using data from a randomized trial. Our formal results leverage a statistical connection between our problem and that of learning heterogeneous treatment effects under endogeneity using an instrumental variable. We find our method to perform well in a number of empirical evaluations. |
| title | Treatment Allocation under Uncertain Costs |
| topic | Methodology |
| url | https://arxiv.org/abs/2103.11066 |