Static supervised learning-in which experimental data serves as a training sample for the
estimation of an optimal treatment assignment policy-is a commonly assumed framework of …

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 problem of learning treatment assignment policies from randomized or observational
data arises in many fields. For example, in personalized medicine, we seek to map patient …

In this work we consider probabilistic approaches to sequential decision making. The
ultimate goal is to provide methods by which decision making problems can be attacked by …

This article improves the existing proven rates of regret decay in optimal policy estimation.
We give a margin-free result showing that the regret decay for estimating a within-class …

Contextual bandit algorithms are sensitive to the estimation method of the outcome model as
well as the exploration method used, particularly in the presence of rich heterogeneity or …

This paper considers the use of a simple posterior sampling algorithm to balance between
exploration and exploitation when learning to optimize actions such as in multiarmed bandit …

Policy learning using historical observational data are an important problem that has
widespread applications. Examples include selecting offers, prices, or advertisements for …

Bandit and reinforcement learning (RL) problems can often be framed as optimization
problems where the goal is to maximize average performance while having access only to …

Abstract The Multi-Armed Bandit problem constitutes an archetypal setting for sequential
decision-making, permeating multiple domains including engineering, business, and …