‘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 the Conditional Average Treatment Effect (CATE) or Individual 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 intervention T on outcome Y for users with observed features X, 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 on Jupyter Notebook of how to use all these algorithms.
Some Use Cases:
- Campaign Targeting Optimization
- Personalized Engagement
The ‘Casual ML’ package currently supports the following methods:
- Tree-based algorithms
- Uplift Random Forests on KL divergence, Euclidean Distance, and Chi-Square
- Uplift Random Forests on Contextual Treatment Selection
- Meta-learner algorithms
Installation (Source: https://causalml.readthedocs.io/en/latest/installation.html )
causalml is available on PyPI, and can be installed from
pip or source as follows:
pip install causalml
git clone https://github.com/uber-common/causalml.git cd causalml python setup.py build_ext --inplace python setup.py install