CausalML: Python Package for Causal Machine Learning
This package addresses the need for accessible tools in causal machine learning for researchers and practitioners, but it is incremental as it repackages existing methods into a software library.
The authors tackled the gap between theoretical causal inference methods and practical applications by developing CausalML, a Python package that implements algorithms combining causal inference and machine learning, making these methods accessible for real-world use.
CausalML is a Python implementation of algorithms related to causal inference and machine learning. Algorithms combining causal inference and machine learning have been a trending topic in recent years. This package tries to bridge the gap between theoretical work on methodology and practical applications by making a collection of methods in this field available in Python. This paper introduces the key concepts, scope, and use cases of this package.