This course presents the theory and practice of contemporary causal inference and its recent applications in and connections to data science and machine learning. Significant advances in causal inference have come from a wide variety of disciplines, including philosophy, economics, statistics, computer science and epidemiology. This course will introduce students to methods from all of these disciplines and will consider what they can add to a wide range of policy debates, from traditional policy impact assessment, through research design criticism and correction, to recent conceptual frameworks of fairness as they illuminate normative policy dilemmas. Because nothing is more practical than a good theory.
This course is for 1st year MDS students only.