Using a small example as an illustration, this article reviews multivariate matching from the
perspective of a working scientist who wishes to make effective use of available methods …

This book builds on and is a sequel to our book Targeted Learning: Causal Inference for
Observational and Experimental Studies (2011). Since the publication of this first book on …

A goal of many health studies is to determine the causal effect of a treatment or intervention
on health outcomes. Often, it is not ethically or practically possible to conduct a perfectly …

Smartphones can unobtrusively capture human behavior and contextual data such as user
interaction and mobility. Thus far, smartphone sensor data have primarily been used to gain …

In this article, we propose a general framework for distribution-free nonparametric testing in
multi-dimensions, based on a notion of multivariate ranks defined using the theory of …

How do Latino immigrants in the United States understand existing racial categories? And
how does the existing US racial order influence this understanding? Using data from the …

This article presents a new method for optimal matching in observational studies based on
mixed integer programming. Unlike widely used matching methods based on network …

Matching for balance, pairing for heterogeneity in an observational study of the effectiveness of
for-profit and not-for-profit Page 1 The Annals of Applied Statistics 2014, Vol. 8, No. 1, 204–231 …

Previous research suggests a link between the quality of teacher–student relationships and
the students' behavioral outcomes; however, the observational nature of past studies makes …

Every newly trained surgeon performs her first unsupervised operation. How do the health
outcomes of her patients compare with the patients of experienced surgeons? Using data …