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：摘要：可靠的因果效应估计要求处理组和对照组在其他方面具有相似性。在观察性研究中，例如电子健康记录的分析，这一条件通常无法得到保证。研究者必须平衡背景变量，使得处理组和对照组在这些变量上具有相似性。常见的方法包括匹配（将个体分组为小的同质集合）或加权（上调或下调个体权重）以形成相似的轮廓。然而，当测量变量众多且并非所有变量对结果具有同等重要性时，创建完全相同的分布可能不可行。联合变量重要性图（jointVIP）软件包通过量化和可视化每个变量与处理和结果的关系，指导研究者决策应优先调整哪些变量。