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MSFT Scientist: Agents, Causal AI & Future of DoWhy | Amit Sharma S2E4 | CausalBanditsPodcast.com

Causal Bandits Podcast

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Exploring Exchangeability and Causal Mechanisms in Data Augmentation

This chapter explores the concept of exchangeability in data distributions, focusing on the importance of recognizing static elements in dynamic environments. It discusses the relationship between data augmentation, causal mechanisms, and algorithms, highlighting methodologies like contrastive learning that utilize static properties to improve learning processes.

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