Essential Causal Inference Techniques for Data Science

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!

• Understand limitations of A/B testing and value of causal inference
• Implement main causal inference techniques in R
• Explore and apply advanced machine learning and causal inference methods

• Basic familiarity with the software or workflow used
• Ability to follow step-by-step instructions in English