  Organ transplantation is often the last resort for treating end-stage
illness, but the probability of a successful transplantation depends greatly on
compatibility between donors and recipients. Current medical practice relies on
coarse rules for donor-recipient matching, but is short of domain knowledge
regarding the complex factors underlying organ compatibility. In this paper, we
formulate the problem of learning data-driven rules for organ matching using
observational data for organ allocations and transplant outcomes. This problem
departs from the standard supervised learning setup in that it involves
matching the two feature spaces (i.e., donors and recipients), and requires
estimating transplant outcomes under counterfactual matches not observed in the
data. To address these problems, we propose a model based on representation
learning to predict donor-recipient compatibility; our model learns
representations that cluster donor features, and applies donor-invariant
transformations to recipient features to predict outcomes for a given
donor-recipient feature instance. Experiments on semi-synthetic and real-world
datasets show that our model outperforms state-of-art allocation methods and
policies executed by human experts.
