UE Learning, Probabilities and Causality

Causality is at the core of our vision of the world and of the way we reason. It has long been recognized as an important concept and was already mentioned in the ancient Hindu scriptures: “Cause is the effect concealed, effect is the cause revealed”. Even Democritus famously proclaimed that he would rather discover a causal relation than be the king of presumably the wealthiest empire of his time. Nowadays, causality is seen as an ideal way to explain observed phenomena and to provide tools to reason on possible outcomes of interventions and what-if experiments, which are central to counterfactual reasoning, as ‘‘what if this patient had been given this particular treatment?’’

The main aim of this course is to provide the principles and tools to understand and master learning models based on probabilities and causality.

Probability and statistics background.

Pattern Recognition and Machine Learning, by C. Bishop, 2005. [link].

An introduction to variational autoencoders, by D. P. Kingma and M. Welling [link].

The matrix cook book.

Dynamical Variational Autoencoders: A Comprehensive Review, by Laurent Girin et. al. 2021

The Book of Why: The New Science of Cause and Effect, by Pearl and Mackenzie, 2018

Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, by Pearl, 1988

Causation, Prediction, and Search, by Spirtes, Glamour and Scheines, 2000

Elements of Causal Inference: Foundations and Learning Algorithms, by Peters, Janzing and Scholkopf, 2017

Causality: Models, Reasoning and Inference, by Pearl, 2009