Profile Matching for the Generalization and Personalization of Causal Inferences

05/20/2021
by   Eric R. Cohn, et al.
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We introduce profile matching, a multivariate matching method for randomized experiments and observational studies that finds the largest possible self-weighted samples across multiple treatment groups that are balanced relative to a covariate profile. This covariate profile can represent a specific population or a target individual, facilitating the tasks of generalization and personalization of causal inferences. For generalization, because the profile often amounts to summary statistics for a target population, profile matching does not require accessing individual-level data, which may be unavailable for confidentiality reasons. For personalization, the profile can characterize a single patient. 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. The method can also be used for the selection of units for study follow-up, and it readily applies to multi-valued treatments with many treatment categories. We evaluate the performance of profile matching in a simulation study of generalization of a randomized trial to a target population. We further illustrate this method in an exploratory observational study of the relationship between opioid use treatment and mental health outcomes. We analyze these relationships for three covariate profiles representing: (i) sexual minorities, (ii) the Appalachian United States, and (iii) a hypothetical vulnerable patient. We provide R code with step-by-step explanations to implement the methods in the paper in the Supplementary Materials.

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