https://pmc.ncbi.nlm.nih.gov/articles/PMC12269483/

Background

Whether vaccination reduces the risk of post-acute sequelae in pediatric populations was poorly characterized, and answering it requires a causal contrast rather than an association. Two obstacles compound each other. Electronic health record confounders number in the hundreds, and the sites contributing data differ in their covariate distributions, so an estimator tuned on one network’s population will not transport cleanly to another.

Method

DisC2o-HD estimates the average treatment effect for high-dimensional data while explicitly accounting for covariate shift across sites. Using the surrogate likelihood approach, it calibrates the propensity score and outcome models so that the desired covariate balancing property is approximately attained under the shifted distributions. The estimator remains consistent if either the propensity score model or the outcome regression is correctly specified, and it attains the semiparametric efficiency bound when both hold.

Main finding

Applied to RECOVER data, the method produced an average treatment effect for vaccination on post-acute outcomes in a diverse pediatric population, a setting where prior evidence had been thin. Because covariate distributions differed materially across contributing sites, an estimator that ignored that shift would have attributed part of the between-site composition difference to the vaccine itself.