Learning Representations Using Causal Invariance - Leon Bottou

Understanding the inductive bias due to dropout - Raman Arora

What is causal inference, and why should data scientists know? by Ludvig Hult

Beyond Empirical Risk Minimization: the lessons of deep learning

Invariant Risk Minimization

Keynote: The Mathematics of Causal Inference: with Reflections on Machine Learning

Knots and Quantum Theory - Edward Witten

ICLR 2021 Keynote - "Geometric Deep Learning: The Erlangen Programme of ML" - M Bronstein

14. Causal Inference, Part 1

Lectures on Causality: Jonas Peters, Part 1

From Classical Statistics to Modern ML: the Lessons of Deep Learning - Mikhail Belkin

Andrej Karpathy: Tesla Autopilot and Multi-Task Learning for Perception and Prediction

Double Machine Learning for Causal and Treatment Effects

05 Imperial's Deep learning course: Equivariance and Invariance

Ferenc Huszár Causal Inference in Everyday Machine Learning Part 1

Keynote: Judea Pearl - The New Science of Cause and Effect

History of Bayesian Neural Networks (Keynote talk)

Causal Inference: Making the Right Intervention | QuantumBlack

Is Optimization the Right Language to Understand Deep Learning? - Sanjeev Arora

The Blessings of Multiple Causes

Causal Reinforcement Learning -- Part 1/2 (ICML tutorial)