causal ml

We have hosted the application causal ml in order to run this application in our online workstations with Wine or directly.

Quick description about causal ml:

Causal ML is a Python package that provides a suite of uplift modeling and causal inference methods using machine learning algorithms based on recent research [1]. It provides a standard interface that allows users to estimate the Conditional Average Treatment Effect (CATE) or Individual Treatment Effect (ITE) from experimental or observational data. Essentially, it estimates the causal impact of intervention T on outcome Y for users with observed features X, without strong assumptions on the model form. An important lever to increase ROI in an advertising campaign is to target the ad to the set of customers who will have a favorable response in a given KPI such as engagement or sales. CATE identifies these customers by estimating the effect of the KPI from ad exposure at the individual level from A/B experiments or historical observational data.

Features:

• A Python Package for Uplift Modeling and Causal Inference with ML
• Documentation available
• Campaign targeting optimization
• Personalized engagement
• Examples available

Programming Language: Python.
Categories: