Covariate-adaptive randomization inference in matched designs

TL;DR

This paper introduces covariate-adaptive randomization inference for matched observational studies, adjusting permutation probabilities based on propensity score differences to improve causal inference accuracy without excluding matched pairs.

Contribution

It develops a new randomization inference method that accounts for propensity score discrepancies, enhancing validity and applicability in matched observational studies.

Findings

Achieves type I error control close to the nominal level with large samples.

Generalizes existing sensitivity analysis methods effectively.

Demonstrates improved inference accuracy through simulations and real data analyses.

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. We address this problem 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. We show that the test achieves type I error control arbitrarily close to the nominal level when large samples are available for propensity score estimation. We characterize the large-sample behavior of the new randomization test for a difference-in-means estimator of a constant additive effect. We also show that existing methods of sensitivity analysis generalize effectively to covariate-adaptive randomization inference. Finally, we evaluate the empirical value of covariate-adaptive randomization procedures via comparisons to traditional uniform inference in matched designs with and without propensity score calipers and regression adjustment using simulations and analyses of genetic damage among welders and right-heart catheterization in surgical patients.