Summary of A Brief Introduction to Causal Inference in Machine Learning, by Kyunghyun Cho
A Brief Introduction to Causal Inference in Machine Learning
by Kyunghyun Cho
First submitted to arxiv on: 14 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel lecture note series for DS-GA 3001.003 “Special Topics in DS – Causal Inference in Machine Learning” aims to bridge the gap between machine learning and causal reasoning, targeting master’s and PhD level students with basic ML knowledge but no prior exposure to causal inference. The course seeks to expand students’ understanding of ML by incorporating causal reasoning, a crucial aspect of out-of-distribution generalization (or lack thereof). By introducing fundamental concepts and methods in causal inference, this lecture note series aims to equip students with the skills to apply causal thinking to real-world problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This lecture note series introduces students to the basics of causal inference in machine learning. It’s designed for master’s and PhD level students who already have a basic understanding of machine learning but want to learn more about how to incorporate causal reasoning into their work. The course focuses on helping students understand how to apply causal thinking to real-world problems. |
Keywords
» Artificial intelligence » Generalization » Inference » Machine learning
