EconML

EconML is a Python package that applies the power of machine learning techniques to estimate individualized causal responses from observational or experimental data. The suite of estimation methods provided in EconML represents the latest advances in causal machine learning. By incorporating individual machine learning steps into interpretable causal models, these methods improve the reliability of what-if predictions and make causal analysis quicker and easier for a broad set of users. It allows users to easily select the best model for their questions and implement them.

The use-cases of this library are:

1. A/B testing: Interpret experiments with imperfect compliance

2. Customer Segmentation: Estimate individualized responses to incentives

3. Multi Investment Attribution: Distinguish the effects of multiple outreach efforts

Resource: Github__