Optimal Full Matching and Related Designs via Network Flows

By Ben B. Hansen, S. Klopfer. 2006

675 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/0427844b4984ed872df32abdd8508a9b670fe9dc

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In Statistics and Probability

Citations in the corpus

Multivariate and Propensity Score Matching Software with Automated Balance Optimization: The Matching Package for R (2008, neutral): “Alternatively one can do optimal full matching (Hansen 2004; Hansen and Klopfer 2006; Rosenbaum 1989, 1991) instead of the 1-to-N matching with replacement which I focus on in this article.”
Effect Modification and Design Sensitivity in Observational Studies (2013, background): “…(i) one treated subject and one or more controls or (ii) one control and one or more treated subjects; see Rosenbaum (1991) for motivation, and see Hansen and Klopfer (2006) and Hansen (2007) for optimal full matching as implemented in R. Full matching can use every available subject, and it is…”
Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation (2020, methodology): “…exact matching (Iacus et al. 2011; Iacus et al. 2012), Propensity Matching - 1 : k propensity score matching, Prognostic Matching - 1 : k prognostic score matching, Full Matching - Full matching (Hansen and Klopfer 2006), Mahal - 1 : k matching on the Mahalanobis distance between covariates, Fast -”
An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies (2011, background): “In R, the Matching (Sekhon, in press), MatchIt (Ho, Imai, King, & Stuart, 2011), and Optmatch (Hansen & Klopfer, 2006) packages allow one to implement a variety of different matching methods.”
Evaluation of subset matching methods and forms of covariate balance (2016, methodology): “We implemented this method using the R functions in the supplementary materials of [8], which in turn uses the R function pairmatch in the optmatch package [29].”
Challenges With Propensity Score Strategies in a High-Dimensional Setting and a Potential Alternative (2011, methodology): “We use three different kinds of matching algorithms: a greedy nearest neighbor (N), optimal pair matching (O), and full matching (F; Hansen and Klopfer, 2006; Rosenbaum, 1991).”
The Effect of a Data Breach Announcement on Customer Behavior: Evidence from a Multichannel Retailer (2018, methodology): “We employ this algorithm because it allows for a more general type of matching of one treatment unit to one or more comparison units and vice versa and thus does not require discarding of any unmatched observations (Hansen and Klopfer 2006; Rosenbaum 2002).”
Imposing Minimax and Quantile Constraints on Optimal Matching in Observational Studies (2017, methodology): “For discussion of optimal matching in observational studies, see Rosenbaum (1989, 2010, Part II), Hansen and Klopfer (2006), Hansen (2007), Stuart (2010), Lu et al. (2011), Yang et al. (2012), Zubizarreta (2012), and Pimentel et al. (2015), and see CONTACT Paul R. Rosenbaum…”
Journal of the American Statistical Association Using Mixed Integer Programming for Matching in an Observational Study of Kidney Failure after Surgery Using Mixed Integer Programming for Matching in an Observational Study of Kidney Failure after Surgery (neutral): “Sixth, using existing inferential techniques is straightforward if matching is done without replacement (Hansen and Klopfer 2006).”
Bayesian Nonparametric Modeling for Causal Inference (2011, methodology): “…comprise combinations of the following features: (1) matching method (nearest neighbor, optimal pairmatching, and full matching (Rosenbaum 1991; Hansen and Klopfer 2006)), (2) ratio of treated to control (1:1, 1:2, 1:3), (3) matching with or without replacement, and (4) method for estimating…”
Prognostic score–based balance measures for propensity score methods in comparative effectiveness research (2013, positive): “1; subclassification and full matching were implemented using the MatchIt and optmatch packages, respectively [18,19].”