In this paper I develop a consistent estimator of the population average treatment effect (PATE) which is based on a nonstandard version of the Oaxaca-Blinder decomposition. As a result, I extend the recent literature which has utilized the treatment effects framework to reinterpret this...

In this paper I use the National Supported Work (NSW) data to examine the finite-sample performance of the Oaxaca–Blinder unexplained component as an estimator of the population average treatment effect on the treated (PATT). Precisely, I follow sample and variable selections from Dehejia and...

In this paper I provide new evidence on the implications of treatment effect heterogeneity for least squares estimation when the effects are inappropriately assumed to be homogenous. I prove that under a set of benchmark assumptions linear regression provides a consistent estimator of the...

In this paper we evaluate the premise from the recent literature on Monte Carlo studies that an empirically motivated simulation exercise is informative about the actual ranking of various estimators when applied to a particular problem. We consider two alternative designs and provide an...

In this paper I develop a new version of the Oaxaca-Blinder decomposition whose unexplained component recovers a parameter which I refer to as the average wage gap. Under a particular conditional independence assumption, this estimand is equivalent to the average treatment effect (ATE). I also...

It is standard practice in applied work to rely on linear least squares regression to estimate the effect of a binary variable ("treatment") on some outcome of interest. In this paper I study the interpretation of the regression estimand when treatment effects are in fact heterogeneous. I show...

It is standard practice in applied work to study the effect of a binary variable ("treatment") on an outcome of interest using linear models with additive effects. In this paper I study the interpretation of the ordinary and two-stage least squares estimands in such models when treatment effects...