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About Towards Causal Representation Learning
This document discusses how machine learning and causal inference can benefit each other. It outlines two key challenges for machine learning: 1) lack of robustness to distribution shifts, and 2) inability to learn reusable mechanisms. Causal models could help with these challenges by encoding the underlying causal structures that generate data and allow interventions and transfer. The document argues that discovering causal representations from data is a central problem at the intersection of machine learning and causality.
Detailed Information
Author: | ['Yishuai Zhang'] |
---|---|
Publication Year: | 2021 |
Pages: | 24 |
Language: | English |
Format: | |
Price: | FREE |
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