A0822
Title: Covariate-adaptive randomization inference in matched designs
Authors: Samuel Pimentel - UC Berkeley (United States) [presenting]
Yaxuan Huang - University of California Berkeley (United States)
Abstract: It is common to conduct causal inference in matched observational studies by proceeding as though treatment assignments within matched sets are assigned uniformly at random and using this distribution as the basis for inference. This approach ignores observed discrepancies in matched sets that may be consequential for the distribution of treatment, which are succinctly captured by within-set differences in the propensity score. This problem is addressed via covariate-adaptive randomization inference, which modifies the permutation probabilities to vary with estimated propensity score discrepancies and avoids requirements to exclude matched pairs or model an outcome variable. It is shown that the test achieves type I error control arbitrarily close to the nominal level when large samples are available for propensity score estimation. The large-sample behaviour of the new randomization test for a difference-in-means estimator of a constant additive effect is characterized. It is also shown that existing sensitivity analysis methods generalize effectively to covariate-adaptive randomization inference. Finally, the empirical value of covariate-adaptive randomization procedures is evaluated via comparisons to traditional uniform inference in matched designs with and without propensity score callipers and regression adjustment using simulations and analysis of genetic damage among welders.
