Fundamentals of modern applied causal inference. The course introduces the basic principles of causal inference and machine learning and shows how the two combine in practice to deliver causal insights and policy implications in real-world datasets, allowing for high-dimensionality and flexible estimation. Lectures provide the foundations of these new methodologies and proofs of their properties, and course assignments involve real-world data (from the social sciences and tech industry) as well as synthetic data analysis based on these methodologies. Prerequisites include mathematical maturity in probability, statistics, optimization, linear algebra, and calculus. Recommended: CS 226 or equivalent.
3 units · Letter or Credit/No Credit
Fundamentals of modern applied causal inference. The course introduces the basic principles of causal inference and machine learning and shows how the two combine in practice to deliver causal insights and policy implications in real-world datasets, allowing for high-dimensionality and flexible estimation. Lectures provide the foundations of these new methodologies and proofs of their properties, and course assignments involve real-world data (from the social sciences and tech industry) as well as synthetic data analysis based on these methodologies. Prerequisites include mathematical maturity in probability, statistics, optimization, linear algebra, and calculus. Recommended: 226 or equivalent.
Offered in Winter 2027 at Stanford University.