IS 790 - Causal Artificial Intelligence

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Causality is an important part of artificial intelligence (AI), machine learning (ML), and data science (DS). The ability to understand causality from data is regarded as one of the major fundamental elements of human-level intelligence. The study of causation guides actions and policies in many applications in science, medicine, and business. Causal inference goes beyond association and prediction, unlike traditional machine learning approaches, by modeling the effect of interventions and is capable of formalizing counterfactual reasoning. The goal of this course is to introduce students to methodologies and algorithms for causal discovery, causal reasoning and connect various aspects of causal inference, including structural causal model, potential outcome framework, inverse probability weighting, effect of intervention, and counterfactuals. Lectures will focus on theoretical foundation, while classwork will consist primarily of practical applications of the methods.
Course ID: 103011
Prerequisite: IS 603 - Decision Technology Systems
Components: Lecture
Grading Method: Regular