S Learner Uplift Model for Individual Treatment Effect and Customer Segmentation in Python | ML
S-learner is a meta-learner that uses a single machine learning model to estimate the individual level causal treatment effect. In this tutorial, we will talk about:
👉 How to implement s-learner in Python?
👉 How to make individual treatment effect (ITE) estimation using an s-learner?
👉 How to segment customers based on the individual treatment effect (ITE) estimation?
⏰ Timecodes ⏰
0:00 - Intro
0:19 - Step 1: Install and Import Libraries
1:11 - Step 2: Create Dataset
1:41 - Step 3: S-Learner Model Training
2:36 - Step 4: S-Learner Model Predictions
3:00 - Step 5: S-Learner Individual Treatment Effect (ITE)
3:26 - Step 6: S-Learner Average Treatment Effect (ATE)
3:48 - Step 7: Customer Segmentation Using S-Learner Individual Treatment Effect (ITE)
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