A Brief Introduction to Causal Inference in Machine Learning

TL;DR

This lecture note introduces causal inference concepts to machine learning students, emphasizing its importance for out-of-distribution generalization and expanding traditional ML perspectives.

Contribution

It provides an accessible introduction to causal inference tailored for students with basic ML background, bridging the gap to causal reasoning.

Findings

Highlights the role of causal inference in improving generalization

Provides foundational knowledge for integrating causal reasoning into ML

Encourages broader understanding of ML limitations and capabilities

Abstract

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.)