The project explores cutting-edge uplift modeling techniques for a marketing client. Uplift modeling is a set of data science techniques that leverage both causal inference and machine learning to predict the effect of a treatment (Gutierrez, 2016). It can be used to understand if a treatment (such as a marketing advertisement) caused the desired outcome (such as a purchase). The project contains both research and application. In result, the project is presented as two components: An in-depth review of uplift modeling and its applications A deep-dive on a special set of uplift methods available in Python called meta-learners.