%0 Journal Article
%A Rosenbaum, Paul R.
%T Modern Algorithms for Matching in Observational Studies
%D 2020
%J Annual Review of Statistics and Its Application,
%V 7
%N Volume 7, 2020
%P 143-176
%@ 2326-831X
%R https://doi.org/10.1146/annurev-statistics-031219-041058
%K assignment algorithm
%K sensitivity analysis
%K network optimization
%K optimal matching
%K integer programming
%K design sensitivity
%K refined balance
%K principal unobserved covariate
%K Mahalanobis distance
%K propensity score
%K causal inference
%K near-fine balance
%K fine balance
%I Annual Reviews,
%X Using a small example as an illustration, this article reviews multivariate matching from the perspective of a working scientist who wishes to make effective use of available methods. The several goals of multivariate matching are discussed. Matching tools are reviewed, including propensity scores, covariate distances, fine balance, and related methods such as near-fine and refined balance, exact and near-exact matching, tactics addressing missing covariate values, the entire number, and checks of covariate balance. Matching structures are described, such as matching with a variable number of controls, full matching, subset matching and risk-set matching. Software packages in R are described. A brief review is given of the theory underlying propensity scores and the associated sensitivity analysis concerning an unobserved covariate omitted from the propensity score.
%U https://www.annualreviews.org/content/journals/10.1146/annurev-statistics-031219-041058