Functional Sequential Treatment Allocation with Covariates
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2020
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| _version_ | 1866909439502581760 |
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| author | Kock, Anders Bredahl Preinerstorfer, David Veliyev, Bezirgen |
| author_facet | Kock, Anders Bredahl Preinerstorfer, David Veliyev, Bezirgen |
| contents | We consider a multi-armed bandit problem with covariates. Given a realization of the covariate vector, instead of targeting the treatment with highest conditional expectation, the decision maker targets the treatment which maximizes a general functional of the conditional potential outcome distribution, e.g., a conditional quantile, trimmed mean, or a socio-economic functional such as an inequality, welfare or poverty measure. We develop expected regret lower bounds for this problem, and construct a near minimax optimal assignment policy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2001_10996 |
| institution | arXiv |
| publishDate | 2020 |
| record_format | arxiv |
| spellingShingle | Functional Sequential Treatment Allocation with Covariates Kock, Anders Bredahl Preinerstorfer, David Veliyev, Bezirgen Machine Learning Econometrics Statistics Theory We consider a multi-armed bandit problem with covariates. Given a realization of the covariate vector, instead of targeting the treatment with highest conditional expectation, the decision maker targets the treatment which maximizes a general functional of the conditional potential outcome distribution, e.g., a conditional quantile, trimmed mean, or a socio-economic functional such as an inequality, welfare or poverty measure. We develop expected regret lower bounds for this problem, and construct a near minimax optimal assignment policy. |
| title | Functional Sequential Treatment Allocation with Covariates |
| topic | Machine Learning Econometrics Statistics Theory |
| url | https://arxiv.org/abs/2001.10996 |