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.)

翻译：本文是为纽约大学数据科学中心2024年春季开设的DS-GA 3001.003课程“数据科学专题——机器学习中的因果推断”编写的讲座笔记。该课程面向具备机器学习基础知识但此前未接触过因果推断或因果推理的硕士及博士研究生，重点引导学生拓展机器学习视角与知识体系，将因果推理纳入其中——这一维度正是所谓"分布外泛化"（及其缺失现象）的核心所在。