The text discusses the transformative potential of uplift modeling, a technique that identifies individuals whose behavior can be positively influenced by specific treatments, offering numerous applications in marketing, healthcare, and more. It delves into tailored uplift decision trees, training processes, model evaluation metrics, and an experimentation that validates the effectiveness of uplift modeling.
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Illustration by the author
Hands-On Approach to Uplift with Tree-Based Models
Predictive analytics has long been a cornerstone of decision-making, but what if we told you there’s 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.
Objective
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
1. Demystifying Uplift Modelling
1.1. Why uplift models?
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 and take proactive measures, such as offering tailored incentives or exclusive promotions.
1.2. What to expect from uplift models?
Uplift models categorize individuals into different classes based on their responses to treatment. The main classes typically include:
- Persuadables
- Sure Things
- Lost Causes
- Sleeping Dogs / Do-Not-Disturb
1.3. What data do uplift models need for training?
Before diving into the theory, let’s look at the data needed to train uplift models:
- Feature columns
- Treatment indicator
- Target column
2. Tailoring Tree-Based Models for Uplift Modelling
2.1. How is uplift defined?
To assess the incremental benefits of applying a treatment, we need to compute the difference in outcomes between receiving the treatment and not receiving it.
2.2. How are tree-based models adapted to uplift modeling?
Traditional decision trees are designed to maximize the accuracy of the predicted class probabilities. Their splitting criteria and pruning methods are adapted toward this objective.
2.3. Application
Here is an example of code that trains an uplift model and makes predictions. It relies on the library developed by Uber, CausalML.
3. Quantifying Uplift Models Performance
3.1. What are the common evaluation metrics?
Evaluating uplift models is significantly different from traditional machine learning. Since it’s impossible to observe the effects of being treated and untreated on an individual simultaneously, there is no ground truth.
3.2. Application
The performance of the model was compared to:
- A baseline involving random assignment
- A solo model using a random forest as the estimator
3.3. Results
This small experiment shows that the uplift model performs better than a solo model on the validation set.
Key Takeaways
- Uplift modeling is a powerful analytical approach that aims to predict the incremental impact of a treatment or intervention on an individual’s behavior.
- It is possible to adapt various machine learning techniques to uplift modeling, with random forests being a prime example.
- However, it is crucial to pay close attention to uplift modeling-specific assumptions.
References
- P. Gutierrez, J.Y. Gérardy, Causal Inference and Uplift Modeling, A review of the literature, 2016
- P. Rzepakowski, S. Jaroszewicz, Decision trees for uplift modeling with single and multiple treatments, 2011
- George Fei, Modeling Uplift Directly: Uplift Decision Tree with KL Divergence and Euclidean Distance as Splitting Criteria, 2019
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