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Existing approaches of using the bootstrap method to derive standard errorand confidence interval of average treatment effect estimate has one potentialissue, which is that they are actually bootstrapping the wrong thing, resultingin unvalid statistical inference. In this paper, we discuss this importantissue and propose a new non-parametric bootstrap method that can more preciselyquantify the uncertainty associated with average treatment effect estimates. Wedemonstrate the validity of this approach through a simulation study and areal-world example, and highlight the importance of deriving standard error andconfidence interval of average treatment effect estimates that both removeextra undesired noise and are easy to interpret when applied in real worldscenarios.

Are we bootstrapping the right thing? A new approach to quantify uncertainty of Average Treatment Effect Estimate