MultiObjMatch: Matching with Optimal Tradeoffs between Multiple Objectives in R

TL;DR

This paper introduces an R package, MultiObjMatch, that implements multi-objective matching using network flow algorithms to optimize tradeoffs among balance, sample size, and similarity in observational studies.

Contribution

The paper presents a new R package for multi-objective matching that efficiently explores tradeoffs among multiple design goals using a network flow approach.

Findings

Demonstrates flexibility in exploring user-defined tradeoffs.

Reanalyzes the National Supported Work dataset.

Analyzes clinical data on diabetic kidney disease.

Abstract

In an observational study, matching aims to create many small sets of similar treated and control units from initial samples that may differ substantially in order to permit more credible causal inferences. The problem of constructing matched sets may be formulated as an optimization problem, but it can be challenging to specify a single objective function that adequately captures all the design considerations at work. One solution, proposed by \citet{pimentel2019optimal} is to explore a family of matched designs that are Pareto optimal for multiple objective functions. We present an R package, \href{https://github.com/ShichaoHan/MultiObjMatch}{\texttt{MultiObjMatch}}, that implements this multi-objective matching strategy using a network flow algorithm for several common design goals: marginal balance on important covariates, size of the matched sample, and average within-pair multivariate distances. We demonstrate the package's flexibility in exploring user-defined tradeoffs of interest via two case studies, a reanalysis of the canonical National Supported Work dataset and a novel analysis of a clinical dataset to estimate the impact of diabetic kidney disease on hospitalization costs.