ORIE 6745 - Causality and Learning for Intelligent Decision Making

Fall. 4 credits. Student option grading.

Prerequisite: familiarity with basic statistics, probability, and calculus, or permission of the instructor. Offered at Cornell Tech in New York City.

N. Kallus.

Some of the most impactful applications of machine learning, whether in online marketing and commerce, personalized medicine, or data-driven policymaking, are not just about prediction, but rather taking the right action directed at the right target at the right time. Actions and decisions, unlike predictions, have consequences and so, in seeking to take the right action, one must seek to understand the causal effects of any action or action policy, whether through active experimentation or analysis of observational data. In this course, we will study the interaction of causality and machine learning for the purpose of (mostly) designing intelligent systems that make decisions. In the case of known causal effects, we will briefly review the theory of generalization as it applies to designing action policies and systems. We will then study causal inference and estimation of unknown causal effects using both classical methods and modern machine learning and optimization methods, considering a variety of settings including controlled experiments (A/B testing), regression discontinuity, instrumental variables, and general observational studies. We will then study the direct design of action policies and systems when causal effects are not known, looking closely both at the online (bandit) and offline (off-policy learning) cases. Finally, we will study ancillary consequences of intelligent systems’ actions, such as algorithmic fairness. The course will culminate in a final project.