Apologies for cross-posting.
Dear Colleagues and Students,
Episode 4 of the INFORMS Service Science Online Forum features the talk by Prof. Dennis Zhang (WashU Olin) on "Personalized Policy Learning through Discrete Experimentation: Theory and Empirical Evidence". Discover how the new Deep Learning for Policy Targeting (DLPT) framework turns limited A/B test data into personalized, continuous policy wins, boosting pricing & incentive decisions with data-driven precision and AI power!
Time: July 14 2025, Monday, 10:00 – 11:00 AM EST
Zoom: https://cuhk.zoom.us/j/97586819348
Speaker: Prof. Dennis Zhang (WashU Olin), Area Editor of Operations Research Machine Learning and Data Science Department
Title: Personalized Policy Learning through Discrete Experimentation: Theory and Empirical Evidence
Abstract: Randomized Controlled Trials (RCTs), or A/B testing, have become the gold standard for optimizing various operational policies on online platforms. However, RCTs on these platforms typically cover a limited number of discrete treatment levels, while the platforms increasingly face complex operational challenges involving optimizing continuous variables, such as pricing and incentive programs. The current industry practice involves discretizing these continuous decision variables into several treatment levels and selecting the optimal discrete treatment level. This approach, however, often leads to suboptimal decisions as it cannot accurately extrapolate performance for untested treatment levels and fails to account for heterogeneity in treatment effects across user characteristics. This study addresses these limitations by developing a theoretically solid and empirically verified framework to learn personalized continuous policies based on high-dimensional user characteristics, using observations from an RCT with only a discrete set of treatment levels. Specifically, we introduce a deep learning for policy targeting (DLPT) framework that includes both personalized policy value estimation and personalized policy learning. We prove that our policy value estimators are asymptotically unbiased and consistent, and the learned policy achieves a √ n-regret bound. We empirically validate our methods in collaboration with a leading social media platform to optimize incentive levels for content creation. Results demonstrate that our DLPT framework significantly outperforms existing benchmarks, achieving substantial improvements in both evaluating the value of policies for each user group and identifying the optimal personalized policy.
Moderator: Prof. Renyu Zhang (CUHK)
For more information, see our website https://sites.google.com/view/service-science-online-forum/, and our YouTube Channel https://www.youtube.com/playlist?list=PLCn8oCTLj5JEeIiA3_ATZp8gtlkWJCRpO.
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Renyu Zhang
Associate Professor
The Chinese University of Hong Kong
Hong Kong
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