Multiple Treatments Uplift Models for Binary Outcome Using Python CausalML | Machine Learning
Multiple treatments sometimes are compared with a control group and with each other in an experiment. The experiment outcome can be continuous results such as sales or binary results such as a response to a promotion.
In this tutorial, we will talk about how to use the python package `causalML` to build meta-learner uplift models for an experiment with multiple treatments and binary outcomes. We will cover:
👉 How to implement S-learner, T-learner, and X-learner on multiple treatments?
👉 How to make an average treatment effect (ATE) estimation for multiple treatments?
👉 How to make individual treatment effects (ITE) for multiple treatments?
👉 How to get the confidence intervals for the average treatment effect (ATE) and individual treatment effect (ITE) estimation?
⏰ Timecodes ⏰
0:00 - Intro
0:52 - Step 1: Install and Import Libraries
1:33 - Step 2: Create a Dataset
3:43 - Step 3: Multiple Treatments Uplift Model Using S Learner
6:47 - Step 4: Multiple Treatments Uplift Model Using T Learner
8:00 - Step 5: Multiple Treatments Uplift Model Using X Learner
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🎞️ Uplift Model playlist: • Uplift Model
📺 Videos mentioned in the video
Multiple Treatments Uplift Model Using Python Package CausalML: • Multiple Treatments Uplift Model Using Pyt...
ATE vs CATE vs ATT vs ATC for Causal Inference: • Average Treatment Effects ATE vs CATE vs A...
S Learner Uplift Model for Individual Treatment Effect and Customer Segmentation in Python: • S Learner Uplift Model for Individual Trea...
Explainable S-Learner Uplift Model Using Python Package CausalML: • Explainable S Learner Uplift Model Using P...
T Learner Uplift Model for Individual Treatment Effect (ITE) in Python: • T Learner Uplift Model for Individual Trea...
Explainable T-learner Deep Learning Uplift Model Using Python Package CausalML: • Explainable T learner Deep Learning Uplift...
X-Learner Uplift Model in Python: • X-Learner Uplift Model in Python | Meta Le...
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