10 citations · 18 across the 8 of their papers we have counts for
12 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…
Learning under random distributional shifts
Kirk Bansak, Elisabeth Paulson, Dominik Rothenhäusler
Many existing approaches for generating predictions in settings with distribution shift model distribution shifts as adversarial or low-rank in suitable representations. In various…
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…
Outcome-Driven Dynamic Refugee Assignment with Allocation Balancing
Kirk Bansak, Elisabeth Paulson
This study proposes two new dynamic assignment algorithms to match refugees and asylum seekers to geographic localities within a host country. The first, currently implemented in a…
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…
Estimating Causal Moderation Effects with Randomized Treatments and Non-Randomized Moderators
Kirk Bansak
Researchers are often interested in analyzing conditional treatment effects. One variant of this is "causal moderation," which implies that intervention upon a third (moderator) va…
Comparative Causal Mediation and Relaxing the Assumption of No Mediator-Outcome Confounding: An Application to International Law and Audience Costs
Kirk Bansak
Experiments often include multiple treatments, with the primary goal to compare the causal effects of those treatments. This study focuses on comparing the causal anatomies of mult…
A Generalized Approach to Power Analysis for Local Average Treatment Effects
Kirk Bansak
This study introduces a new approach to power analysis in the context of estimating a local average treatment effect (LATE), where the study subjects exhibit noncompliance with tre…