Sliding into Causal Inference, with Python

The course provides a gentle introduction to Causal Inference using Python, aimed at data practitioners with basic knowledge of Python and statistics. The focus is on nurturing an intuitive understanding of the subject and identifying causal inference problems. By the end of the course, learners should be able to recognize causal inference problems, understand different tools to address them, and be motivated to further their learning. The course covers the Fundamental Problem of Causal Inference, tools like Differences-in-Differences, Propensity score methods, and Synthetic Controls. The teaching method emphasizes intuitive understanding over complex theory, with a mix of theory and practical Python code. The intended audience is data practitioners interested in causal inference.