In this talk, we introduce profile matching, a new multivariate matching method for covariate adjustment in randomized experiments and observational studies that finds the largest possible self-weighted samples across multiple treatments groups that are balanced relative to a covariate profile. This covariate profile is flexible and can represent a population or individual, facilitating the generalization, transportation, and personalization of causal inferences. Profile matching achieves covariate balance by construction, but unlike existing approaches to matching, it does not require specifying a matching ratio, as this is implicitly optimized for the data. Also, profile matching does not require accessing individual-level data of the target population; instead, the target population can be characterized by summary statistics in the covariate profile. We evaluate the performance of profile matching in a simulation study generalizing results from a randomized trial to a target population. We illustrate the utility of this method in an exploratory observational study of opioid use and mental health.