Explainable S Learner Uplift Model Using Python Package CausalML | Machine Learning
S-learner is a meta-learner uplift model that uses a single machine learning model to estimate the individual level causal treatment effect. In this tutorial, we will talk about how to use the python package `causalML`to build s-learner. We will cover:
👉 How to implement s-learner using the Python package CausalML?
👉 How to make individual treatment effect (ITE) and average treatment effect (ATE) estimation using s-learner?
👉 How to check s-learner feature importance?
👉 How to interpret an s-learner uplift model using SHAP?
⏰ Timecodes ⏰
0:00 - Intro
0:27 - Step 1: Install and Import Libraries
1:22 - Step 2: Create Dataset
1:56 - Step 3: S-Learner Average Treatment Effect (ATE)
3:12 - Step 4: S-Learner Individual Treatment Effect (ITE)
4:03 - Step 5: S-Learner Model Feature Importance
5:57 - Step 6: S-Learner Model Interpretation
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