Primary Submission Category: Matching
Treatment bootstrapping: a new approach to quantify uncertainty of average treatment effect estimates
Authors: Jing Li,
Presenting Author: Jing Li*
This paper proposes a new non-parametric bootstrap method to quantify the uncertainty of average treatment effect estimates from matching estimators. More specifically, it seeks to quantify the uncertainty associated with the average treatment effect estimate for the treated by bootstrapping the treatment group only and finding the counterpart control group by matching on estimated propensity score. We demonstrate the validity of this approach and compare it with existing bootstrap approaches through Monte Carlo simulation and real world example data. The results indicate that the proposed approach constructs confidence intervals that have comparable precision and coverage rate as existing bootstrap approaches and can produce smaller standard error estimates albeit with lower coverage rate depending on the proportion of treatment group units in the sample data and the specific matching method used.