Essential Causal Inference Techniques for Data Science
By External Provider
About this course
Data scientists often get asked questions related to causality: (1) did recent PR coverage drive sign-ups, (2) does customer support increase sales, or (3) did improving the recommendation model drive revenue? Supporting company stakeholders requires every data scientist to learn techniques that can answer questions like these, which are centered around issues of causality and are solved with causal inference. In this project, you will learn the high level theory and intuition behind the four main causal inference techniques of controlled regression, regression discontinuity, difference in difference, and instrumental variables as well as some techniques at the intersection of machine learning and causal inference that are useful in data science called double selection and causal forests. These will help you rigorously answer questions like those above and become a better data scientist!
What you will learn
- Learn the limitations of AB testing and why causal inference techniques can be powerful.
- Understand the intuition behind and how to implement the four main causal inference techniques in R.
- Explore newer methods at the intersection of causal inference and machine learning and implement them in R.