CausalML: Python Package for Causal Machine Learning

Abstract

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.

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• To do or not to do: cost-sensitive causal decision-making
• Counterfactual Learning to Rank using Heterogeneous Treatment Effect Estimation
• Adaptive Multi-Source Causal Inference