Most AI/ML systems in production rely on predictive models. Businesses score users for churn risk, conversion likelihood, or click probability, and then make decisions based on those scores. But accurate predictions do not imply better decisions. A model that answers the question “Who will convert anyway?” very well is often a terrible guide for deciding who to target with an offer, notification, or intervention. More often than not, what we really care about is answering the question “Whose behavior will actually change because of our intervention?” rather than “Who will do X?”.
This gap between prediction and decision-making shows up in more and more places, such as marketing campaigns, product experiments, and even healthcare and risk interventions. Uplift modeling is a way to bridge this gap by estimating the causal effect of an intervention at the individual level—who is persuadable, who is a sure thing, who is a lost cause, and who might even be harmed or annoyed if we intervene. At Intuit, we use uplift modeling to enhance targeting and experimentation, helping us allocate marketing and product interventions more effectively across millions of customers.
In this talk, I will introduce uplift modeling as a practical form of causal AI that fits into existing experimentation and ML workflows. I will start by contrasting predictive and causal thinking, followed by core concepts like treatment effects and counterfactuals, using concrete examples. After that, I will dive into uplift modeling techniques, specifically using meta-learners in Python. Finally, we will go over evaluation techniques that are unique to uplift modeling and causal inference, and how they differ from standard ML metrics like F1 score or AUC.
This talk is aimed at data scientists and ML engineers at any level, including those with no background in causal inference. Basic familiarity with Python and machine learning concepts is helpful but not required. Attendees will leave with a clear mental model of when predictive models are not enough, an intuitive understanding of uplift modeling and its causal foundations, and a sense of how to start applying uplift models in their own AI/ML systems to target the right people.
