QUANTITATIVE RESEARCH METHODS WORKSHOP
Abstract: When running multi-arm trials, experimenters may wish to both learn and evaluate data-driven policies; for example, learning which version of treatment is most effective and evaluating the effect of that treatment in comparison to a control condition. Response adaptive algorithms, which dynamically update treatment assignment mechanisms based on observed response, facilitate experimental designs where the most data is collected about the most effective interventions, and can improve policy learning over conventional randomized trials. I discuss design decisions when running adaptive experiments, and considerations for inference when using adaptively collected data. I review applications to Facebook Messenger studies using different adaptive algorithms.
Molly Offer-Westort is an Assistant Professor in the Department of Political Science at The University of Chicago. She works on quantitative methodology for social science research, with a focus on causal inference, machine learning, and experimental design–particularly for adaptive experiments. Her PhD is from Yale, joint in Political Science and Statistics & Data Science. Previously, Molly was a post-doctoral fellow in Susan Athey’s Golub Capital Social Impact Lab at the Stanford Graduate School of Business. In addition to the PhD, she hold a Masters in Statistics, also from Yale, and a Masters in Public Affairs, from the Princeton School of Public and International Affairs.
This workshop is open to the Yale community. To receive announcements and invitations to attend, please subscribe at https://csap.yale.edu/quantitative-research-methods-workshop.
The series is sponsored by the ISPS Center for the Study of American Politics and The Whitney and Betty MacMillan Center for International and Area Studies at Yale with support from the Edward J. and Dorothy Clarke Kempf Fund.