We often talk about correlation vs causation in theory but while implementing Data Science solutions towards solving business problems not much influence is given to validating causation amongst independent and dependent features. Conventional Machine Learning methods identify patterns in existing data to make predictions and they always result in retrieving some underlying patterns even if they are not real and factitious. The assumption is these patterns are same in training, testing, validation data and deployed environments. However, if these patterns change for some reason, ML models fail. The reasons could be numerous like a distribution shift, external unexpected factor etc. Causal Machine Learning helps by defining treatment condition with respective control data to make causal inferences guiding machine learning models to pay attention to cause and effect relations.

Uplift modelling is a predictive modelling technique that uses machine learning models to estimate the treatment's incremental effect at the user level. It's frequently used for personalizing product offerings, as well as targeting promotions and advertisements. In the context of causal inference, in this article, we will discuss the uplift modelling, its types of modelling and lastly, we will see how a Python-based package called CausalML can be used to address the causal inference. Following are the major points to be discussed in this article. Let's start the discussion by understanding the uplift modelling.

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.

'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.