uplift model

Multiple Treatments Uplift Models for Binary Outcome Using Python CausalML Multiple treatments ITE/CATE and ATE estimation using meta-learner uplift models S-learner, T-learner, and X-learner in Python for binary outcome data

Multiple Treatments Uplift Models for Binary Outcome Using Python CausalML

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 …

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Explainable S-Learner Uplift Model Using Python Package CausalML Uplift model using meta-learner s-learner for heterogeneous ITE, ATE, model explainability, and feature importance

Explainable S Learner Uplift Model Using Python Package CausalML

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: Resources for this post: Let’s get started! Step 1: Install and Import Libraries In step 1, …

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T Learner Uplift Model for Individual Treatment Effect (ITE) in Python Manually create two models for meta-learner T-learner: Model data processing, model training, prediction, and ITE/ATE calculation

T Learner Uplift Model for Individual Treatment Effect (ITE) in Python

T-learner is a meta-learner that uses two machine learning models to estimate the individual level heterogeneous causal treatment effect. In this tutorial, we will talk about: Resources for this post: Let’s get started! Step 0: T-learner Algorithm T-learner follows three steps to estimate individual treatment effect (ITE): To learn how to make an individual treatment …

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Explainable T-learner Deep Learning Uplift Model Using Python Package CausalML T-learner uplift models using XGBoost, lightGBM, and neural network model with feature importance and model interpretation

Explainable T-learner Deep Learning Uplift Model Using Python Package CausalML

T-learner is a meta-learner that uses two machine learning models to estimate the individual-level heterogeneous causal treatment effect. In this tutorial, we will talk about how to use the python package causalML to build a T-learner. We will cover: Resources for this post: If you are interested in building a T-learner manually, please check out my previous …

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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 …

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