meta learner

S Learner Uplift Model for Individual Treatment Effect and Customer Segmentation in Python Uplift model using meta-learner s-learner for heterogeneous individual treatment effect (ITE) and marketing customer segmentation

S Learner Uplift Model for Individual Treatment Effect and Customer Segmentation in Python

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: Resources for this post: Let’s get started! Step 1: Install and Import Libraries In step 1, we will install and import the python libraries. Firstly, let’s install causalml for synthetic dataset …

S Learner Uplift Model for Individual Treatment Effect and Customer Segmentation in Python Read More »

X-Learner Uplift Model in Python Manually create meta-learner X-learner: Model data processing, model training, prediction, individual treatment effect (ITE) and average treatment effect (ATE) calculation, and customer segmentation

X-Learner Uplift Model in Python

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: Resources for this post: Let’s get started! Step 0: X-learner Algorithm X-learner is consisted of three stages, and each stage has model(s) that …

X-Learner Uplift Model in Python Read More »