Traditional randomized experiments allow us to determine the overall causal impact of a treatment program (e.g. marketing, medical, social, education, political). Uplift modeling (also known as true lift, net lift, incremental lift) takes a further step to identify individuals who are truly positively influenced by a treatment through data mining / machine learning. This technique allows us to identify the “persuadables” and thus optimize target selection in order to maximize treatment benefits. This important subfield of data mining/data science/business analytics has gained significant attention in areas such as personalized marketing, personalized medicine, and political election with plenty of publications and presentations appeared in recent years from both industry practitioners and academics.
In this workshop, I will introduce the concept of Uplift, review existing methods, contrast with the traditional approach, and introduce a new method that can be implemented with standard software. A method and metrics for model assessment will be recommended. Our discussion will include new approaches to handling a general situation where only observational data are available, i.e. without randomized experiments, using techniques from causal inference. Additionally, an integrated modeling approach for uplift and direct response (where it can be identified who actually responded, e.g., click-through or coupon scanning) will be discussed. Last but not least, extension to the multiple treatment situation with solutions to optimizing treatments at the individual level will also be discussed. While the talk is geared towards marketing applications (“personalized marketing”), the same methodologies can be readily applied in other fields such as insurance, medicine, education, political, and social programs. Examples from the retail and non-profit industries will be used to illustrate the methodologies.