This course provides an introduction to the fundamental concepts and statistical methods used in causal inference. Understanding causal relationships is essential in fields such as economics, digital marketing, healthcare, and policy evaluation. For example, students will explore how to assess the impact of a new product design on user experience, a price change on sales, or a welfare incentive on employee performance. The course equips students with essential tools for revealing causal effects and conducting hypothesis tests using both experimental and observational data. Students will also gain hands-on experience with real-world data sets. Special topics may include A/B testing on digital platforms and cutting-edge methods that connect causal inference with machine learning.