Social capital—the strength of an individual's social network and community—has been
identified as a potential determinant of outcomes ranging from education to health …

Methods for cluster-robust inference are routinely used in economics and many other
disciplines. However, it is only recently that theoretical foundations for the use of these …

Dyadic data, where outcomes reflecting pairwise interaction among sampled units are of
primary interest, arise frequently in social science research. Regression analyses with such …

Optimal transport (OT) is a versatile framework for comparing probability measures, with
many applications to statistics, machine learning and applied mathematics. However, OT …

Many economic activities are embedded in networks: sets of agents and the (often) rivalrous
relationships connecting them to one another. Input sourcing by firms, interbank lending …

We propose a bootstrap procedure for data that may exhibit cluster‐dependence in two or
more dimensions. The asymptotic distribution of the sample mean or other statistics may be …

Limit theorems for distributions invariant under groups of transformations Page 1 The Annals of
Statistics 2022, Vol. 50, No. 4, 1960–1991 https://doi.org/10.1214/21-AOS2165 © Institute of …

This article investigates double/debiased machine learning (DML) under multiway clustered
sampling environments. We propose a novel multiway cross-fitting algorithm and a multiway …

The overwhelming majority of empirical research that uses cluster-robust inference assumes
that the clustering structure is known, even though there are often several possible ways in …

We discuss when and how to deal with possibly clustered errors in linear regression models.
Specifically, we discuss situations in which a regression model may plausibly be treated as …