Text

Functional Sequential Treatment Allocation

Consider a setting in which a policy maker assigns subjects to treatments, observing each outcome before the next subject arrives. Initially, it is unknown which treatment is best, but the sequential nature of the problem permits learning about the effectiveness of the treatments. While the multi-armed-bandit literature has shed much light on the situation when the policy maker compares the effectiveness of the treatments through their mean, much less is known about other targets. This is restrictive, because a cautious decision maker may prefer to target a robust location measure such as a quantile or a trimmed mean. Furthermore, socio-economic decision making often requires targeting purpose specific characteristics of the outcome distribution, such as its inherent degree of inequality, welfare or poverty. In the present article, we introduce and study sequential learning algorithms when the distributional characteristic of interest is a general functional of the outcome distribution. Minimax expected regret optimality results are obtained within the subclass of explore-then-commit policies, and for the unrestricted class of all policies. Supplementary materials for this article are available online.

Ketersediaan

Informasi Detail

Judul Seri

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION (JASA); Vol.117 No.539 September 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 1311-1323

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

ROBUSTNESS
MULTI-ARMED BANDITS
RANDOMIZED CONTROLLED TRIALS
DISTRIBUTIONAL CHARACTERISTICS
MINIMAX OPTIMAL EXPECTED REGRET
SEQUENTIAL TREATMENT ALLOCATION

Info Detail Spesifik

https://doi.org/10.1080/01621459.2020.1851236

Pernyataan Tanggungjawab

Anders Bredahl Kock, David Preinerstorfer, Bezirgen Veliyev

Versi lain/terkait

Lampiran Berkas

Komentar