no code implementations • 5 Jun 2023 • Kirk Bansak, Elisabeth Paulson, Dominik Rothenhäusler
Thus, we consider a class of random distribution shift models that capture arbitrary changes in the underlying covariate space, and dense, random shocks to the relationship between the covariates and the outcomes.
no code implementations • 27 Jul 2020 • Jeremy Ferwerda, Nicholas Adams-Cohen, Kirk Bansak, Jennifer Fei, Duncan Lawrence, Jeremy M. Weinstein, Jens Hainmueller
Instead, they often rely on availability heuristics, which can lead to the selection of sub-optimal landing locations, lower earnings, elevated outmigration rates, and concentration in the most well-known locations.
no code implementations • 2 Jul 2020 • Kirk Bansak, Elisabeth Paulson
On this dataset, we find that the allocation balancing algorithm can achieve near-perfect balance over time with only a small loss in expected employment compared to the pure employment-maximizing algorithm.
no code implementations • 20 Feb 2019 • Avidit Acharya, Kirk Bansak, Jens Hainmueller
We introduce a constrained priority mechanism that combines outcome-based matching from machine-learning with preference-based allocation schemes common in market design.
