Knowledge of causal effects is of great importance to decision makers in a wide variety of
settings. In many cases, however, these causal effects are not known to the decision makers …

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 …

Importance Many medical journals, includingJAMA, restrict the use of causal language to the
reporting of randomized clinical trials. Although well-conducted randomized clinical trials …

ABSTRACT Full automation using Artificial Intelligence (AI) predictions may not be optimal if
humans can access contextual information. We study human-AI collaboration using an …

In this Element, which continues our discussion in Foundations, the authors provide an
accessible and practical guide for the analysis and interpretation of Regression …

We study deep neural networks and their use in semiparametric inference. We establish
novel nonasymptotic high probability bounds for deep feedforward neural nets. These …

In this article, we study methods for estimating causal effects in settings with panel data,
where some units are exposed to a treatment during some periods and the goal is …

This article introduces an intuitive and easy-to-implement nonparametric density estimator
based on local polynomial techniques. The estimator is fully boundary adaptive and …

Using more than 7.1 million implicit and explicit attitude tests drawn from US participants to
the Project Implicit website, we examined long-term trends across 14 years (2007–2020) …

Purpose This paper aims to present the Difference-in-Differences (DiD) method in an
accessible language to a broad research audience from a variety of management-related …