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

'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.