Causal inference has recently attracted substantial attention in the machine learning and
artificial intelligence community. It is usually positioned as a distinct strand of research that …

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 …

Traditional machine learning paradigms are based on the assumption that both training and
test data follow the same statistical pattern, which is mathematically referred to as …

We present a new estimator for causal effects with panel data that builds on insights behind
the widely used difference-in-differences and synthetic control methods. Relative to these …

Program evaluation methods are widely applied in economics to assess the effects of policy
interventions and other treatments of interest. In this article, we describe the main …

We apply causal forests to a dataset derived from the National Study of Learning Mindsets,
and discusses resulting practical and conceptual challenges. This note will appear in an …

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 …

The synthetic control method (SCM) is a popular approach for estimating the impact of a
treatment on a single unit in panel data settings. The “synthetic control” is a weighted …

This work considers the question of how convenient access to copious data impacts our
ability to learn causal effects and relations. In what ways is learning causality in the era of …

Generalized random forests Page 1 The Annals of Statistics 2019, Vol. 47, No. 2, 1148–1178
https://doi.org/10.1214/18-AOS1709 © Institute of Mathematical Statistics, 2019 GENERALIZED …