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This paper proposes a new non-parametric bootstrap method to quantify theuncertainty of average treatment effect estimate for the treated from matchingestimators. More specifically, it seeks to quantify the uncertainty associatedwith the average treatment effect estimate for the treated by bootstrapping thetreatment group only and finding the counterpart control group by pair matchingon estimated propensity score without replacement. We demonstrate the validityof this approach and compare it with existing bootstrap approaches throughMonte Carlo simulation and analysis of a real world data set. The resultsindicate that the proposed approach constructs confidence intervals andstandard errors that have 95 percent or above coverage rate and betterprecision compared with existing bootstrap approaches, while these measuresalso depend on percent treated in the sample data and the sample size.

Treatment bootstrapping: A new approach to quantify uncertainty of average treatment effect estimates