Harvard University

Automated Data-Adaptive Analytics to Improve Robustness of Confounding Control when Estimating Treatment Effects in Electronic Healthcare Databases

Routinely-collected healthcare databases generated from insurance claims and electronic health records have tremendous potential to provide information on the real-world effectiveness and safety of medical products. However, unmeasured confounding …

Evaluating the performance of advanced causal inference methods applied to healthcare claims data

Cohort studies of treatments developed from healthcare claims often have hundreds of thousands of patients and up to several thousand measured covariates. Therefore, new causal inference methods that combine ideas from machine learning and causal …

Evaluating the performance of advanced causal inference methods applied to healthcare claims data

Cohort studies of treatments developed from healthcare claims often have hundreds of thousands of patients and up to several thousand measured covariates. Therefore, new causal inference methods that combine ideas from machine learning and causal …