UCPH Statistics Seminar: Dominik Rothenhäusler

Title: Random Distribution Shift in Refugee Placement: Strategies for Building Robust Models

Speaker: Dominik Rothenhäusler from Stanford University

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. In this talk, we compare three different modeling strategies: the standard approach described above, an approach that uses newer data and proxy outcomes, and a hybrid approach. We show 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.