Profile Matching for the Generalization and Personalization of Causal Inferences

TL;DR

The paper introduces profile matching, a versatile covariate balancing method for causal inference that enhances generalization and personalization in experiments and observational studies, applicable even with limited data access.

Contribution

It presents a novel multivariate matching approach that balances covariates without predefined ratios, suitable for multiple treatments, and demonstrates its effectiveness through simulations and real-world case studies.

Findings

Effective in generalizing trial results to target populations.

Facilitates personalized causal inference for individuals.

Applicable to multi-valued treatments and observational data.

Abstract

We introduce profile matching, a multivariate matching method for randomized experiments and observational studies that finds the largest possible unweighted 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 generalization and personalization of causal inferences. For generalization, because the profile often amounts to summary statistics for a target population, profile matching does not always require accessing individual-level data, which may be unavailable for confidentiality reasons. For personalization, the profile comprises the characteristics of a single individual. 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 the 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 and mental health outcomes. We analyze these relationships for three covariate profiles representing: (i) sexual minorities, (ii) the Appalachian United States, and (iii) the characteristics of a hypothetical vulnerable patient. The method can be implemented via the new function profmatch in the designmatch package for R, for which we provide a step-by-step tutorial.