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Causal ML is a Python package that deals with uplift modeling, which estimates heterogeneous treatment effect (HTE) and causal inference methods with the help of machine learning (ML) algorithms based on research. It uses a standard interface that allows users to estimate theConditional Average Treatment Effect(CATE) orIndividual Treatment Effect(ITE) from data (experimental or observational).

Casual ML package provides eight cutting edge uplift modeling algorithms combining causal inference & ML. Essentially, it estimates the causal impact of interventionTon outcomeYfor users with observed featuresX, without strong assumptions on the model form. As mentioned earlier, the package deals with uplift modeling, which estimates heterogeneous treatment effect (HTE), therefore starting with general causal inference, then learning about HTE and uplift modeling would definitely help.

The Github repository contains a good example onJupyter Notebookof how to use all these algorithms.

Some Use Cases:

The Casual ML package currently supports the following methods:

Github: https://github.com/uber/causalml

Documentation: https://causalml.readthedocs.io/en/latest/about.html

Read: Using Causal Inference to Improve the Uber User Experience

Installation (Source: https://causalml.readthedocs.io/en/latest/installation.html )

causalmlis available on PyPI, and can be installed frompipor source as follows:

Frompip:

From source:

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Asif Razzaq is an AI Tech Blogger and Digital Health Business Strategist with robust medical device and biotech industry experience and an enviable portfolio in development of Health Apps, AI, and Data Science. An astute entrepreneur, Asif has distinguished himself as a startup management professional by successfully growing startups from launch phase into profitable businesses. This has earned him awards including, the SGPGI NCBL Young Biotechnology Entrepreneurs Award.

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This Python Package 'Causal ML' Provides a Suite of Uplift Modeling and Causal Inference with Machine Learning - MarkTechPost