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In this talk we introduce uplift modeling, a method to estimate causal effects of a treatment, e.g. a marketing campaign, to effectively target customers that are most likely to respond to it. We describe the most common methods to estimate such effects by working a concrete example. Specifically, meta-algorithms to estimate conditional average treatment effect (CATE) using machine learning estimators.


Dr. Juan Orduz

Mathematician & Data Scientist with interest in statistical learning, bayesian and geometric methods.

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