ORIE 5751

Applied Causal Inference using Machine Learning

Course Description

Course information provided by the Courses of Study 2024-2025.

Provides an applied treatment of modern causal inference using machine learning to handle high-dimensionality and nonparametric estimation. Formulates key causal questions in the languages of structural equation modeling and potential outcomes. Presents methods for estimating and constructing confidence intervals on causal and structural parameters using machine learning, including de-biased machine learning, and for learning how to predict heterogeneous treatment effects. Introduces tools from machine learning and deep learning developed for prediction purposes and discusses how to adapt them to causal inference. Emphasizes the applied and practical perspectives with real-world-data examples and assignments. Requires basic knowledge of statistics and machine learning and programming experience in R or Python.

When Offered Spring.

Permission Note Enrollment limited to: Cornell Tech students.
Prerequisites/Corequisites Prerequisite: ORIE 5750 or CS 5785 and working knowledge of calculus, probability, and linear algebra as well as a modern scripting language such as Python.

Comments Students are expected to have taken a first course in machine learning and have working knowledge of calculus, probability, and linear algebra as well as a modern scripting language such as Python.

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