Causal inference is a critical research topic across many domains, such as statistics,
computer science, education, public policy, and economics, for decades. Nowadays …

This chapter reviews the recent developments at the intersection of personalization and AI in
marketing and related fields. We provide a formal definition of personalized policy and …

Multi-armed bandits a simple but very powerful framework for algorithms that make
decisions over time under uncertainty. An enormous body of work has accumulated over the …

A fundamental challenge in contextual bandits is to develop flexible, general-purpose
algorithms with computational requirements no worse than classical supervised learning …

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 …

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 …

We consider the general (stochastic) contextual bandit problem under the realizability
assumption, that is, the expected reward, as a function of contexts and actions, belongs to a …

The contextual bandit literature has traditionally focused on algorithms that address the
exploration–exploitation tradeoff. In particular, greedy algorithms that exploit current …

In the classical multi-armed bandit problem, instance-dependent algorithms attain improved
performance on" easy" problems with a gap between the best and second-best arm. Are …

A central problem in sequential decision making is to develop algorithms that are practical
and computationally efficient, yet support the use of flexible, general-purpose models …