B1049
Title: Random distribution shift in refugee placement: Strategies for building robust models
Authors: Dominik Rothenhaeusler - Stanford University (United States) [presenting]
Kirk Bansak - Berkeley (United States)
Elisabeth Paulson - Harvard Business School (United States)
Abstract: Algorithmic assignment of refugees and asylum seekers to locations within host countries has gained attention in recent years, with implementations in the US and Switzerland. These approaches use data on past arrivals to generate machine learning models that can be used (along with assignment algorithms) to match families to locations, with the goal of maximizing a policy-relevant integration outcome, such as employment status after a certain duration. Existing implementations and research train models to predict the policy outcome directly, and use these predictions in the assignment procedure. However, the merits of this approach, particularly in non-stationary settings, have not been previously explored. Three different modeling strategies are compared: the standard approach described above, an approach that uses newer data and proxy outcomes, and a hybrid approach. It is shown that the hybrid approach is robust to both distribution shift and weak proxy relationships, the failure points of the other two methods, respectively. These insights support the development of a real-world recommendation tool currently used by NGOs and government agencies.
