Credible causal effect estimation requires treated subjects and controls to be otherwise similar. In observational settings, such as analysis of electronic health records, this is not guaranteed. Investigators must balance background variables so they are similar in treated and control groups. Common approaches include matching (grouping individuals into small homogeneous sets) or weighting (upweighting or downweighting individuals) to create similar profiles. However, creating identical distributions may be impossible if many variables are measured, and not all variables are of equal importance to the outcome. The joint variable importance plot (jointVIP) package to guides decisions about which variables to prioritize for adjustment by quantifying and visualizing each variable's relationship to both treatment and outcome.

翻译：可靠的因果效应估计要求处理组与对照组在其他方面具有相似性。在观察性研究中（例如电子健康记录分析），这一条件通常无法自动满足。研究者必须平衡背景变量，使处理组和对照组的变量分布相似。常见方法包括匹配（将个体分组为同质小集合）或加权（对个体进行上加权或下加权）以生成相似轮廓。然而，当测量变量数量众多且并非所有变量对结局具有同等重要性时，构建完全相同的分布可能难以实现。联合变量重要性图（jointVIP）包通过量化并可视化每个变量与处理及结局的关系，指导如何优先选择需要调整的变量。