In this tutorial article, we aim to provide the reader with the conceptual tools needed to get
started on research on offline reinforcement learning algorithms: reinforcement learning …

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

By transferring knowledge from large, diverse, task-agnostic datasets, modern machine
learning models can solve specific downstream tasks either zero-shot or with small task …

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 …

Flexible estimation of heterogeneous treatment effects lies at the heart of many statistical
applications, such as personalized medicine and optimal resource allocation. In this article …

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 …

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 …

In this paper, we discuss recent developments in econometrics that we view as important for
empirical researchers working on policy evaluation questions. We focus on three main …

In many real-world reinforcement learning applications, access to the environment is limited
to a fixed dataset, instead of direct (online) interaction with the environment. When using this …

Machine-learning prediction methods have been extremely productive in applications
ranging from medicine to allocating fire and health inspectors in cities. However, there are a …