This module provides an overview of causal inference, starting with an introduction and lessons from real world examples. It then explores how companies use online A/B testing for personalization, key tenets and ethical considerations of controlled experiments, and concludes with methods for working with observational data, including the difference-in-difference method.

This module covers the setup of Randomized Field Trials (RFTs), including hypothesis development, evaluation criteria, parameters, and treatment and control groups. It also addresses the importance of randomization, the selection of randomization units, and best practices for designing robust experiments.

This module focuses on high-level needs identification, including understanding user journey maps, identifying business needs, and prioritizing them using PICK charts. It also covers SCQ analysis for referral bonuses in online word-of-mouth (WOM) and breaks down problems into subproblems, identifying common themes, and constructing an issue tree.

This module covers results mapping by designing field experiments that align with the key questions that the business is interested in. It also delves into experiment design, focusing on hypothesis development and identifying control and treatment groups. The module concludes with a final project plan review.