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…

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Citations in the corpus

FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference (2017, methodology): “on a manually dened distance measure. Network ow problems also cannot directly handle constraints; the user needs to manually manipulate the algorithm in order to obtain desired balance constraints (Zubizarreta, 2012). The problems with network ow optimization highlight an important concern about no”
Imposing Minimax and Quantile Constraints on Optimal Matching in Observational Studies (2017, methodology): “R packages optmatch, nbpMatching, and rcbalance at CRAN,finebalance archived butworking atCRAN, andmipmatch available from Zubizarreta (2012).”
A Minimax Surrogate Loss Approach to Conditional Difference Estimation (2018, methodology): “two-separate-models approach if their model is chosen to be an indicator for control times a linear combination of variables plus an indicator for treatment times a linear combination of variables. 3 [28, 27, 8, 37, 20, 21] are dierent in that the matching is done prior to the modeling, with some ex”
Optimal Multilevel Matching in Clustered Observational Studies: A Case Study of the School Voucher System in Chile (2014, methodology): “Following Zubizarreta (2012), these specifications for covariate balance may not only require mean balance, but perhaps also other forms of distributional balance such as fine balance (Rosenbaum et al. 2007), x-fine balance (Zubizarreta et al. 2011), and strength-k matching (Hsu et al. 2014).”
Minimal dispersion approximately balancing weights: asymptotic properties and practical considerations (2017, background): “…theoretical results developed in this work can be extended to matching, where covariates are balanced approximately, but with weights that encode an assignment between matched units (e.g., Rubin, 1973; Rosenbaum, 1989; Hansen, 2004; Abadie & Imbens, 2006; Zubizarreta, 2012; Diamond & Sekhon, 2013).”
Estimation of causal effects with multiple treatments: a review and new ideas (2017, methodology): “With binary treatment, algorithms like full matching (Rosenbaum, 1991) and mixed integer programming (Zubizarreta, 2012) were proposed to optimally match units such that the difference in the covariates’ distributions between the two treatment groups is minimized while retaining most of the units.”
Matching for Balance (2019, methodology): “Examples of these methods are Zubizarreta (2012), Diamond and Sekhon (2013), and Nikolaev et al. (2013); other related examples are Fogarty et al. (2015), Pimentel et al. (2015) and Kallus (2016).”
Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation (2020, methodology): “MIPs have previously been used for causal inference in order to accomodate linear balance constraints on the covariates (Zubizarreta 2012; Zubizarreta et al. 2014; Morucci et al. 2018).”
Evaluation of subset matching methods and forms of covariate balance (2016, methodology): “||At the present, exact and approximate solutions to the cardinality matching problem can be found with the package designmatch for R [10, 33].”
Generalized Optimal Matching Methods for Causal Inference (2016, methodology): “These include optimal-caliper matching (OCM), which GOM with respect to an averaged Lipschitz norm; coarsened exact matching (CEM) [5], which is GOM with respect to the L1 norm on piece-wise linear functions; methods that use mean matching, near-fine balance, and combinations thereof with pair match”
Stable Weights that Balance Covariates for Estimation With Incomplete Outcome Data (2015, methodology): “This is done by defining indicators for its quantiles and balancing the means (see Zubizarreta 2012 for a related discussion in the context of bipartite matching in observational studies).”
Optimal Prescriptive Trees (2019, background): “Since its first usage, several authors (Zubizarreta 2012, Morgan and Winship 2014, Wager andAthey 2018) have used it in their research for benchmarkingmethods.”
MALTS: Matching After Learning to Stretch (2018, methodology): “A flexible setup for producing high quality matches is the optimal matching literature [20], which started with network flow algorithms and has evolved to use integer programming to produce matches that are constrained in user-defined ways [13, 14, 16, 17, 19, 28, 29].”
Matching for Balance, Pairing for Heterogeneity in an Observational Study of the Effectiveness of For-Profit and Not-For-Profit High Schools in Chile (2014, background): “of the variety of statistical properties that may be induced through balance constraints of different types, see Zubizarreta (2012). The user of cardinality matching specifies K constraints of the form (1).”
Hypothesis Tests That Are Robust to Choice of Matching Method (2018, methodology): “ed as some linear function of the matching indicators. While it is technically true that not all tness functions can be encoded in this way, many popular constraints for matching can be. For example, Zubizarreta (2012) gives linear constraint formulations for a number of popular match quality constr”
Optimal Multilevel Matching in Clustered Observational Studies: A Case Study of the Effectiveness of Private Schools Under a Large-Scale Voucher System (2014, methodology): “integer programming, which allows us to enforce different forms of balance for different covariates (Zubizarreta 2012; Zubizarreta et al. 2014).”