Causality is a complex concept, which roots its developments across several fields, such as
statistics, economics, epidemiology, computer science, and philosophy. In recent years, the …

Many empirical questions in economics and other social sciences depend on causal effects
of programs or policies. In the last two decades, much research has been done on the …

Selecting an appropriate set of confounders for which to control is critical for reliable causal
inference. Recent theoretical and methodological developments have helped clarify a …

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

Supplement to “Automated versus Do-It-Yourself Methods for Causal Inference: Lessons
Learned from a Data Analysis Competition”. The online supplement contains the full set of …

Outcome-Wide Longitudinal Designs for Causal Inference: A New Template for Empirical
Studies Page 1 Statistical Science 2020, Vol. 35, No. 3, 437–466 https://doi.org/10.1214/19-STS728 …

This chapter discusses how applied researchers in corporate finance can address
endogeneity concerns. We begin by reviewing the sources of endogeneity—omitted …

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

This book presents the econometric analysis of single-equation and simultaneous-equation
models in which the jointly dependent variables can be continuous, categorical, or …

This paper considers causal inference and sample selection bias in nonexperimental
settings in which (i) few units in the nonexperimental comparison group are comparable to …