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Credible causal effect estimation requires treated subjects and controls tobe otherwise similar. In observational settings, such as analysis of electronichealth records, this is not guaranteed. Investigators must balance backgroundvariables so they are similar in treated and control groups. Common approachesinclude matching (grouping individuals into small homogeneous sets) orweighting (upweighting or downweighting individuals) to create similarprofiles. However, creating identical distributions may be impossible if manyvariables are measured, and not all variables are of equal importance to theoutcome. The joint variable importance plot (jointVIP) package to guidesdecisions about which variables to prioritize for adjustment by quantifying andvisualizing each variable's relationship to both treatment and outcome.

JOINTVIP: Prioritizing variables in observational study design with joint variable importance plot in R