We discuss the relevance of the recent machine learning (ML) literature for economics and
econometrics. First we discuss the differences in goals, methods, and settings between the …

Abstract Fairness of Artificial Intelligence (AI) decisions has become a big challenge for
governments, companies, and societies. We offer a theoretical contribution to consider AI …

Decision-making in the face of uncertainty is a significant challenge in machine learning,
and the multi-armed bandit model is a commonly used framework to address it. This …

I believe that machine learning (ML) will have a dramatic impact on the field of economics
within a short time frame. Indeed, the impact of ML on economics is already well underway …

Throughout the coronavirus disease 2019 (COVID-19) pandemic, countries have relied on a
variety of ad hoc border control protocols to allow for non-essential travel while safeguarding …

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 …

Research in operations management has traditionally focused on models for understanding,
mostly at a strategic level, how firms should operate. Spurred by the growing availability of …

The increasing impact of algorithmic decisions on people's lives compels us to scrutinize
their fairness and, in particular, the disparate impacts that ostensibly color-blind algorithms …

In recent years, the multi-armed bandit (MAB) framework has attracted a lot of attention in
various applications, from recommender systems and information retrieval to healthcare and …

In many settings, a decision maker wishes to learn a rule, or policy, that maps from
observable characteristics of an individual to an action. Examples include selecting offers …