Hypothesis Tests That Are Robust to Choice of Matching Method

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FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference (2017, methodology): “To handle the problem of finding suboptimal 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; Zubizarreta and Keele, 2017; Resa and Zubizarreta, 201”
A Theory of Statistical Inference for Ensuring the Robustness of Scientific Results (2018, methodology): “Figure 10, reproduced from Morucci et al. (2018), shows the hacking intervals of the p -value of McNemar’s test for different numbers of matched groups.”
Interpretable Almost-Exact Matching for Causal Inference (2019, methodology): “Recent integer-programming-based methods considers extreme matches for all possible reasonable distance metrics, but this is computationally expensive and relies on manual effort to create the ranges (Morucci et al., 2018; NoorE-Alam and Rudin, 2015); in contrast we use machine learning to create a ”
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).”