5 papers
AI-based matching improves refugee employment in a double-blind randomized trial
Kirk Bansak, Jens Hainmueller, Dominik Hangartner +8
Refugee integration is a central policy challenge for host countries, and where governments initially place refugees shapes their integration trajectories. Yet placement officers o…
Robustness of Refugee-Matching Gains to Off-Policy Evaluation Choices
Kirk Bansak, Elisabeth Paulson, Dominik Rothenhäusler +3
Previous research has investigated the potential of refugee matching for boosting refugee outcomes, first considered by Bansak et al. (2018). This paper demonstrates the stability…
A Dual Perspective on Decision-Focused Learning: Scalable Training via Dual-Guided Surrogates
Paula Rodriguez-Diaz, Kirk Bansak Elisabeth Paulson
Many real-world decisions are made under uncertainty by solving optimization problems using predicted quantities. This predict-then-optimize paradigm has motivated decision-focused…
CTRL Your Shift: Clustered Transfer Residual Learning for Many Small Datasets
Gauri Jain, Dominik Rothenhäusler, Kirk Bansak +1
Machine learning (ML) tasks often utilize large-scale data that is drawn from several distinct sources, such as different locations, treatment arms, or groups. In such settings, pr…
Dynamic Matching with Post-allocation Service and its Application to Refugee Resettlement
Kirk Bansak, Soonbong Lee, Vahideh Manshadi +2
Motivated by our collaboration with a major refugee resettlement agency in the U.S., we study a dynamic matching problem where each new arrival (a refugee case) must be matched imm…