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Illustration by the authorHands-On Approach to Uplift with Tree-Based Models

Predictive analytics has long been a cornerstone of decision-making, but what if we told you theres an alternative beyond forecasting? What if you could strategically influence the outcomes instead?

Uplift modeling holds this promise. It adds an interesting dynamic layer to traditional predictions by identifying individuals whose behavior can be influenced positively if they receive special treatments.

The application use cases are endless. In medicine, it would help identify patients for whom a medical treatment could improve their health. In retail, such a model allows for better targeting of customers for whom a promotion or personalized offering would be effective in retention.

This article is the first part of a series that explores the transformative potential of uplift modeling, shedding light on how it can reshape strategies in marketing, healthcare, and beyond. It focuses on uplift models based on decision trees and uses, as a case study, the prediction of customer conversion with the application of promotional offers

After reading this article, you will understand:

No prior knowledge is required to understand the article.The experimentations described in the article were carried out using the libraries scikit-uplift, causalml and plotly. You can find the code here on GitHub.

The best way to understand the benefit of using uplift models is through an example. Imagine a scenario where a telecommunications company aims to reduce customer churn.

A traditional ML-based approach would consist of using a model trained on historical data to predict the likelihood of current customers to churn. This would help identify customers at risk

Read the rest here:

Beyond Predictions: Uplift Modeling & the Science of Influence (Part I) - Towards Data Science