Causality and Invariant Machine Learning

This lecture covers the concepts of causality, correlation, and spurious correlations in machine learning, emphasizing the importance of distinguishing between them. It discusses the problems that arise when machine learning models ignore causation and the biases that can result from spurious correlations. The goal is to remove these spurious correlations through bias mitigation techniques and causal inference. Additionally, the lecture explores the idea of invariance across environments and the development of invariant models to improve robustness, reduce bias, and enhance generalization in machine learning applications.