Explores the importance of causality for robust machine learning, covering ideal datasets, missing data problems, graphical models, and interference models.
Explores the application of machine learning in medicine, emphasizing interpretability, variability between patients, and the quest for transparent equations in medical models.
Delves into causality in an indeterministic world, challenging traditional views and exploring the implications of quantum physics on randomness and reality.
