What is Uplift modelling and how can it be done with CausalML?

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Uplift modelling is a predictive modelling technique that uses machine learning models to estimate the treatment's incremental effect at the user level. It's frequently used for personalizing product offerings, as well as targeting promotions and advertisements. In the context of causal inference, in this article, we will discuss the uplift modelling, its types of modelling and lastly, we will see how a Python-based package called CausalML can be used to address the causal inference. Following are the major points to be discussed in this article. Let's start the discussion by understanding the uplift modelling.

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