X-Learner Uplift Model in Python | Meta Learner | Machine Learning
X-learner is a meta-learner that is an extension of the T-learner. Compared with T-learner, X-learner is better for highly imbalanced treatment and control groups. In this tutorial, we will talk about the following:
👉 How to implement X-learner manually in Python?
👉 How to make an individual treatment effect (ITE) estimation using X-learner?
👉 How to calculate average treatment effect (ATE) estimation using X-learner?
⏰ Timecodes ⏰
0:00 - Intro
0:22 - Step 0: X-learner Algorithm
1:44 - Step 1: Install and Import Libraries
2:38 - Step 2: Pull Data
3:07 - Step 3: X-learner Model Data Processing
3:52 - Step 4: X-Learner 1st Stage Models on Outcomes
4:43 - Step 5: X-Learner 1st Stage Model Predictions on Outcomes
5:11 - Step 6: X-Learner 2nd Stage Models on Imputed ITE
5:58 - Step 7: X-Learner 2nd Stage Model Predictions on Imputed ITE
6:16 - Step 8: X-Learner Propensity Score Model
6:42 - Step 9: X-learner Individual Treatment Effect (ITE)
8:16 - Step 10: X-Learner Average Treatment Effect (ATE)
8:39 - Step 11: Customer Segmentation Using T-learner Individual Treatment Effect (ITE)
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