Optimal Multilevel Matching in Clustered Observational Studies: A Case Study of the School Voucher System in Chile

By Luke Keele, José R. Zubizarreta. 2014

8 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.

1. Sudeepa Roy
2. Cynthia Rudin
3. Alexander Volfovsky
4. Harsh Parikh
5. Tong Wang
6. Berk Ustun
7. Nadia Burkart
8. Lesia Semenova

Citations in the corpus

• 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, background): “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].”