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

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 …

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 …

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 …

This paper provides an assessment of the early contributions of machine learning to
economics, as well as predictions about its future contributions. It begins by briefly …

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 …

We consider the off-policy estimation problem of estimating the expected reward of a target
policy using samples collected by a different behavior policy. Importance sampling (IS) has …

An introduction to a broad range of topics in deep learning, covering mathematical and
conceptual background, deep learning techniques used in industry, and research …