Prioritizing Variables for Observational Study Design using the Joint Variable Importance Plot
利用联合变量重要性图确定观测研究设计的变量优先级
Observational studies of treatment effects require adjustment for confounding variables. However, causal inference methods typically cannot deliver perfect adjustment on all measured baseline variables, and there is often ambiguity about which variables should be prioritized. Standard prioritization methods based on treatment imbalance alone neglect variables' relationships with the outcome. We propose the joint variable importance plot to guide variable prioritization for…