6 citations · 6 across the 5 of their papers we have counts for
6 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 Response to Recent Critiques of Hainmueller, Mummolo and Xu (2019) on Estimating Conditional Relationships
Jens Hainmueller, Jiehan Liu, Ziyi Liu +2
Simonsohn (2024a) and Simonsohn (2024b) critique Hainmueller, Mummolo and Xu (2019, HMX), arguing that failing to model nonlinear relationships between the treatment and moderator…
Identifying Opportunities to Improve the Network of Immigration Legal Services Providers
Vasil Yasenov, David Hausman, Michael Hotard +5
Immigration legal services providers (ISPs) are a principal source of support for low-income immigrants seeking immigration benefits. Yet there is scant quantitative evidence on th…
Leveraging the Power of Place: A Data-Driven Decision Helper to Improve the Location Decisions of Economic Immigrants
Jeremy Ferwerda, Nicholas Adams-Cohen, Kirk Bansak +4
A growing number of countries have established programs to attract immigrants who can contribute to their economy. Research suggests that an immigrant's initial arrival location pl…
Combining Outcome-Based and Preference-Based Matching: A Constrained Priority Mechanism
Avidit Acharya, Kirk Bansak, Jens Hainmueller
We introduce a constrained priority mechanism that combines outcome-based matching from machine-learning with preference-based allocation schemes common in market design. Using rea…