From Correlation to Causation: Data Science for Decision Making

Catalog description

While machine learning models are capable of exploiting correlations within high-dimensional data to perform predictions, uncovering the causal mechanisms — understanding if and how an intervention X causes an outcome Y — is vital for informed decision-making in business and policy. This course builds upon a foundation of basic statistics and programming to explore the essential principles of causal inference within data science. It provides practical training in applying causal inference techniques. First, the course will introduce tools for understanding causal structures, including graphical causal models. The course will then cover key methodologies in causal inference, such as propensity score matching, difference-in-differences, synthetic control methods, and more advanced techniques like instrumental variable (IV) estimations and causal machine learning models. This course serves as an introduction to the cutting-edge field of causal inference, with a focus on project-based and hands-on learning approach. | Knowledge of Python and basic statistics are preferred.