When estimating causal effects using observational data, it is desirable to replicate a
randomized experiment as closely as possible by obtaining treated and control groups with …

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 …

We show that propensity score matching (PSM), an enormously popular method of
preprocessing data for causal inference, often accomplishes the opposite of its intended …

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 …

Featuring engaging examples from diverse disciplines, this book explains how to use
modern approaches to quasi-experimentation to derive credible estimates of treatment …

Measuring the causal effects of digital advertising remains challenging despite the
availability of granular data. Unobservable factors make exposure endogenous, and …

This study examines the impact on student achievement of implementing a bundle of best
practices from high-performing charter schools into low-performing, traditional public …

We present new evidence on the randomization methods used in existing experiments, and
new simulations comparing these methods. We find that many papers do not describe the …

We attempt to clarify, and suggest how to avoid, several serious misunderstandings about
and fallacies of causal inference. These issues concern some of the most fundamental …

This article describes a series of school-based field experiments in over 200 urban schools
across three cities designed to better understand the impact of financial incentives on …