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 …

The propensity score is a common tool for estimating the causal effect of a binary treatment
in observational data. In this setting, matching, subclassification, imputation or inverse …

Propensity score matching (PSM) has become a popular technique for estimating average
treatment effects (ATEs) in accounting research. In this study, we discuss the usefulness and …

Fully updated to reflect the most recent changes in the field, the Second Edition of
Propensity Score Analysis provides an accessible, systematic review of the origins, history …

We discuss a method for improving causal inferences called “Coarsened Exact
Matching”(CEM), and the new “Monotonic Imbalance Bounding”(MIB) class of matching …

In this second edition of Counterfactuals and Causal Inference, completely revised and
expanded, the essential features of the counterfactual approach to observational data …

Imprisonment is the most severe punishment in democratic societies except for capital
punishment, which is used only in the United States. Crime prevention is its primary …

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 …

In this article we develop the theoretical properties of the propensity function, which is a
generalization of the propensity score of Rosenbaum and Rubin. Methods based on the …

This study examines whether Chief Executive Officer (CEO) equity‐based holdings and
compensation provide incentives to manipulate accounting reports. While several prior …