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 observational studies. Since not all variables are equally relevant to the outcome, the plot adds outcome associations to quantify the potential confounding jointly with the standardized mean difference. To enhance comparisons on the plot between variables with different confounding relationships, we also derive and plot bias curves. Variable prioritization using the plot can produce recommended values for tuning parameters in many existing matching and weighting methods. We showcase the use of the joint variable importance plots in the design of a balance-constrained matched study to evaluate whether taking an antidiabetic medication, glyburide, increases the incidence of C-section delivery among pregnant individuals with gestational diabetes.

翻译：治疗效果观察性研究需要调整混杂变量。然而，因果推断方法通常无法对所有测量到的基线变量进行完美调整，且常存在关于哪些变量应优先处理的模糊性。基于治疗失衡的标准优先化方法忽略了变量与结局的关系。我们提出联合变量重要性图，以指导观察性研究中的变量优先排序。由于并非所有变量都与结果同等相关，该图通过添加结果关联性来量化潜在混杂因素，并结合标准化均值差。为增强图中具有不同混杂关系的变量间的比较，我们还推导并绘制了偏倚曲线。利用该图进行变量优先排序可为许多现有匹配和加权方法中的调优参数提供推荐值。我们通过展示联合变量重要性图在平衡约束匹配研究设计中的应用，评估抗糖尿病药物格列本脲是否增加妊娠期糖尿病患者剖宫产的发生率。