“…These projections can be sensitive to modeling choices that affect the accuracy of the treatment effect estimates [Kreif et al, 2016]. Further, the units within a matched group can be far from each other in covariate space -i.e., the matched groups are generally not auditable [Parikh et al, 2022b]. To date, the only observational causal inference techniques that attempt to optimize accuracy while maintaining auditability are those stemming from the almost-matchingexactly (AME) framework, namely optimal matching (optMatch) [Yu et al, 2021, Kallus, 2017, genetic matching (GenMatch) [Diamond and Sekhon, 2013], FLAME/DAME [Wang et al, 2017, Dieng et al, 2019, MALTS [Parikh et al, 2022b[Parikh et al, , 2019[Parikh et al, , 2022a and AHB [Morucci et al, 2020] algorithms.…”