A Brief Introduction to Causal Inference in Machine Learning
It provides foundational education for graduate students in data science, but is incremental as it is a teaching material based on existing concepts.
This lecture note introduces causal inference in machine learning to address the problem of out-of-distribution generalization, aiming to expand students' knowledge by incorporating causal reasoning into their ML understanding.
This is a lecture note produced for DS-GA 3001.003 "Special Topics in DS - Causal Inference in Machine Learning" at the Center for Data Science, New York University in Spring, 2024. This course was created to target master's and PhD level students with basic background in machine learning but who were not exposed to causal inference or causal reasoning in general previously. In particular, this course focuses on introducing such students to expand their view and knowledge of machine learning to incorporate causal reasoning, as this aspect is at the core of so-called out-of-distribution generalization (or lack thereof.)