We propose inverse probability weighted estimators for the distribution functions of the potential outcomes of a binary treatment under the unconfoundedness assumption. We also apply the inverse mapping on the distribution functions to obtain the quantile functions. We show that the proposed...

We construct a Kolmogorov-Smirnov test for the null hypothesis that the conditional average treatment effect is non-negative conditional on every possible value of the covariates. The null hypothesis can be characterized as a conditional moment inequality under the unconfoundedness assumption,...

We present a new Stata command, bmte (bias-minimizing treatment effects), that implements two new estimators proposed in Millimet and Tchernis (2013, Journal of Applied Econometrics 28: 982–1017) and designed to estimate the effect of treatment when selection on unobserved variables exists and...

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...

This paper evaluates and compares the direct effects of two Swedish active labour market programmes for youth, namely youth practice and labour market training. Effectiveness of the programmes is measured by subsequent annual earnings, re-employment probability, and probability of regular...

We characterize the bias of propensity score based estimators of common average treatment effect parameters in the case of selection on unobservables. We then propose a new minimum biased estimator of the average treatment effect. We assess the finite sample performance of our estimator using...

Currently available asymptotic results in the literature suggest that matching estimators have higher variance than reweighting estimators. The extant literature comparing the finite sample properties of matching to specific reweighting estimators, however, has concluded that reweighting...

The use of propensity score methods for program evaluation with non-experimental data typically requires the propensity score be estimated, often with a model whose specification is unknown. While theoretical results suggest that estimators utilizing more flexible propensity score specifications...

We characterize the bias of propensity score based estimators of common average treatment effect parameters in the case of selection on unobservables. We then propose a new minimum biased estimator of the average treatment effect. We assess the finite sample performance of our estimator using...

One particularly vexing puzzle for economists and policymakers over the past several decades concerns the empirical significance of the theoretically predicted pollution haven hypothesis. While neoclassical theory and conventional wisdom both surmise that local economies will suffer deleterious...