Evaluation of subset matching methods and forms of covariate balance

57 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference (2017, background): “…match assignments, some newer matching schemes use mixed integer programming, which is a ﬂexible framework that can accommodate linear balance constraints (Zubizarreta, 2012; Zubizarreta et al., 2014; Keele and Zubizarreta, 2014; Resa and Zubizarreta, 2016; Noor-E-Alam and Rudin, 2015b,a).”
MALTS: Matching After Learning to Stretch (2018, methodology): “These are built on network ﬂow algorithms and integer programming to produce matches that are constrained in user-deﬁned ways (Zubizarreta 2012, Zubizarreta et al. 2014, Keele and Zubizarreta 2017, Resa and Zubizarreta 2016, Kallus 2017, Morucci et al. 2022).”
Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation (2020, methodology): “ction, nearly all other matching and weighting methods require some user-specied input for dening distances (e.g., Zubizarreta, 2012; Pimentel et al., 2018; Keele and Pimentel, 2019; de los Angeles Resa and Zubizarreta, 2016; Rosenbaum, 2017). One formulation of our approach relies on solving a mixe”