Year: 2013
Type: preprint
Abstract: We propose a simple way to improve the efficiency of the average treatment effect on propensity score based estimators. As the weights become arbitrarily large for the propensity scores being close to one or zero, we propose to shrink the propensity scores away from these boundaries. Using a comprehensive Monte Carlo study we show that this simple method substantially reduces the mean squared error of the estimators in finite samples.
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
Funder: Deutsche Forschungsgemeinschaft
OpenAlex ID: W3121451837