We consider the problem of finding Pareto efficient allocations that adhere to quota, eligibility, and priority constraints. We characterize this as a weighted bipartite matching problem with carefully chosen weights. This flexible formulation allows us to consider many problem extensions. We present three such extentions; for each we exhibit a clear dichotomy in which one possible extension is handled by a straightforward modification of our algorithm while a closely related extension is NP-hard.
Oct 18, 2022
We consider the problem of finding Pareto efficient allocations that adhere to quota, eligibility, and priority constraints. We show that this problem can be encoded as a weighted bipartite matching problem with carefully chosen weights. This framework provides us the flexibility to enforce additional criteria in our selected allocations, including notions of fairness.
Jun 6, 2022
We propose unbiased estimators for the total treatment effect in settings with network interference. We use a low-degree assumption on the potential outcomes to establish bounds on the variance of our estimators. In settings where the network is unknown, we leverage a staggered rollout experimental design. Beyond our formal guarantees, our estimators are shown to work well in our experiments on simulated data.
May 23, 2022