Tutorial on Uplift Modeling: How to Optimize using Uplift Predictive Models and Uplift Prescriptive Analytics


Randomized controlled trial (RCT) is known as the gold standard to measure the overall treatment effect of a program (e.g. marketing, medical, social, education, political, economic). Uplift modeling takes a further step to identify individuals who are positively influenced by a treatment or intervention (e.g., a promotion, medical treatment, or policy) through machine learning and predictive modeling by uncovering heterogeneous treatment effects. This methodology allows us to identify the “persuadables” and thus optimize target selection in order to maximize treatment impact. This important subfield of data science or analytics has gained tremendous attention in recent years in application areas such as personalized marketing, personalized medicine, political election, and healthcare programs with plenty of publications and presentations from both industry practitioners and academics.

In this tutorial, I will provide an introduction to the concept of Uplift, compare with traditional response modeling, and review various approaches to Uplift Modeling. The discussion will include approaches to handling a more general situation where only non-experimental (or observational) data are available, integrating causal inference techniques with uplift modeling. We will then discuss the multiple treatment situation to determine the optimal treatment for each individual, which requires constrained optimization using estimates from uplift modeling as inputs. Due to the high uncertainty of lift estimates, various optimization methods will be introduced to handle the uncertainty. While this tutorial is geared towards marketing type applications (“personalized marketing”), the same set of methodologies can be readily applied in other fields such as medicine, insurance, education, political, and social programs. Examples from multiple industries will be used to illustrate its application and methodologies.


Victor has managed teams of quantitative analysts in multiple organizations. He is currently Senior Vice President, Data Science and Artificial Intelligence in Workplace Investing at Fidelity Investments. Previously he managed advanced analytics / data science teams in Personal Investing, Corporate Treasury, Managerial Finance, and Healthcare and Total Well-being at Fidelity Investments. Prior to Fidelity, he was VP and Manager of Modeling and Analysis at FleetBoston Financial (now Bank of America), and Senior Associate at Mercer Management Consulting (now Oliver Wyman).

For academic services, Victor is an elected board member of the National Institute of Statistical Sciences (NISS), where he provides guidance to the board and general education to the statistics community. He has also been a visiting research fellow and corporate executive-in-residence at Bentley University, as well as serving on the steering committee of the Boston Chapter of the Institute for Operations Research and the Management Sciences (INFORMS). Victor earned a master’s degree in Operational Research at Lancaster University, UK, and a PhD in Statistics at the University of Hong Kong, and was a Postdoctoral Fellow in Management Science at University of British Columbia. He has co-authored a graduate level econometrics book and published numerous articles in Data Science, Marketing, Statistics, and Management Science literature. and is co-authoring a graduate-level data science textbook titled “Cause-and-Effect Business Analytics.”

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