Covariate-adjusted Fisher randomization tests for the average treatment effect

Fisher's randomization test (FRT) delivers exact p-values under the strong null hypothesis of no treatment effect on any units whatsoever and allows for flexible covariate adjustment to improve the power. Of interest is whether the resulting covariate-adjusted procedure could also be valid for testing the weak null hypothesis of zero average treatment effect. To this end, we evaluate two general strategies for conducting covariate adjustment in FRTs: the pseudo-outcome strategy that uses the residuals from an outcome model with only the covariates as the pseudo, covariate-adjusted outcomes to form the test statistic, and the model-output strategy that directly uses the output from an outcome model with both the treatment and covariates as the covariate-adjusted test statistic. Based on theory and simulation, we recommend using the ordinary least squares (OLS) fit of the observed outcome on the treatment, centered covariates, and their interactions for covariate adjustment, and conducting FRT with the robust t-value of the treatment as the test statistic. The resulting FRT is finite-sample exact for testing the strong null hypothesis, asymptotically valid for testing the weak null hypothesis, and more powerful than the unadjusted counterpart under alternatives, all irrespective of whether the linear model is correctly specified or not. We start with complete randomization, and then extend the theory to cluster randomization, stratified randomization, and rerandomization, respectively, giving a recommendation for the test procedure and test statistic under each design. Our theory is design-based, also known as randomization-based, in which we condition on the potential outcomes but average over the random treatment assignment.

Duke Scholars

Author Anqi Zhao Fuqua School of Business