Abstract:

Understanding treatment effect heterogeneity has become an increasingly popular task in various fields. For example, in e-commerce, understanding treatment effect heterogeneity helps decision-makers to design personalized advertising strategies to maximize profits. In biomedical studies, learning the impact of treatment on diverse patient subgroups provides insights for personalized care. While much of the existing work in this research area has focused on either analyzing observational data based on untestable causal assumptions or conducting post hoc analyses of existing randomized controlled trial data, little work has gone into designing randomized experiments specifically for uncovering treatment effect heterogeneity. In this work, we develop a unified adaptive experimental design framework towards better learning treatment effect heterogeneity by efficiently identifying subgroups with enhanced treatment effects from a frequentist viewpoint. The adaptive nature of our framework allows practitioners to sequentially allocate experimental efforts adapting to the accrued evidence during the experiment. The resulting design framework can not only complement A/B tests in e-commerce but also unify enrichment designs and response adaptive randomization designs in clinical settings. Our theoretical investigations illustrate the trade-offs between complete randomization and our adaptive experimental algorithms. We also investigate our design in simulation studies and synthetic e-commerce and biomedical data analyses.

ZOOM INVITATION

Topic: Colloquium -- Department of Statistics and Actuarial Science -- University of Iowa
Time: Apr 20, 2023 03:15 PM Central Time (US and Canada)

Join Zoom Meeting
https://uiowa.zoom.us/j/91969622615

Meeting ID: 919 6962 2615
One tap mobile
+13017158592,,91969622615# US (Washington DC)
+13092053325,,91969622615# US

Dial by your location
        +1 301 715 8592 US (Washington DC)
        +1 309 205 3325 US
        +1 312 626 6799 US (Chicago)
        +1 646 876 9923 US (New York)
        +1 646 931 3860 US
        +1 669 444 9171 US
        +1 669 900 6833 US (San Jose)
        +1 719 359 4580 US
        +1 253 215 8782 US (Tacoma)
        +1 346 248 7799 US (Houston)
        +1 386 347 5053 US
        +1 564 217 2000 US
Meeting ID: 919 6962 2615
Find your local number: https://uiowa.zoom.us/u/ah4uecMkA

Join by SIP
91969622615@zoomcrc.com

Join by H.323
162.255.37.11 (US West)
162.255.36.11 (US East)
115.114.131.7 (India Mumbai)
115.114.115.7 (India Hyderabad)
213.19.144.110 (Amsterdam Netherlands)
213.244.140.110 (Germany)
103.122.166.55 (Australia Sydney)
103.122.167.55 (Australia Melbourne)
64.211.144.160 (Brazil)
69.174.57.160 (Canada Toronto)
65.39.152.160 (Canada Vancouver)
207.226.132.110 (Japan Tokyo)
149.137.24.110 (Japan Osaka)
Meeting ID: 919 6962 2615