2 papers
stat.ML2025
Assessment of the conditional exchangeability assumption in causal machine learning models: a simulation study
Gerard T. Portela, Jason B. Gibbons, Sebastian Schneeweiss +1
Observational studies developing causal machine learning (ML) models for the prediction of individualized treatment effects (ITEs) seldom conduct empirical evaluations to assess th…
stat.ME2025
Assessing treatment effects in observational data with missing confounders: A comparative study of practical doubly-robust and traditional missing data methods
Brian D. Williamson, Chloe Krakauer, Eric Johnson +13
In pharmacoepidemiology, safety and effectiveness are frequently evaluated using readily available administrative and electronic health records data. In these settings, detailed co…