Abstract: 

In this session, we will present a brief introduction to causality in AI. We will start with fundamental statistics, introduce causal graphs, do-calculus, and general methods for causal inference. We will give an overview of methods that allow the discovery of causal structures from observational data, as well as several applications in business settings. This session will cover topics from the very basic (what is a correlation?) to more complex ideas such as causal representation learning. This is meant to serve as a general and practical overview of the fastest-growing research area in AI.

Session Outline:

* Pearson to Pearl: A brief intro to causality and statistics
-- What are correlations,
-- Measuring Association vs measuring causal strength
-- Causal Graphs and structural causal equations
-- Structural causal equations
* Discovering causality
-- Conditional independence
-- The chicken and the egg problem
-- Basic causal discovery algorithms
* Applications
-- Why should you care about causality?
-- Simpson's paradoxes everywhere
-- Asking counterfactual questions
-- Recourse
-- Optimization
* The future of AI

Background Knowledge:

Basic knowledge of statistics, and a reasonable understanding of association measures

Bio: 

Andre joined causaLens from Goldman Sachs, where he was an executive director in the Model Risk Management group in Hong Kong and Frankfurt. Today he is working with industry leading, global organisations to apply cutting edge Causal AI research in production level solutions that empower individuals and teams to make better decisions. Andre received his PhD in theoretical physics from the University of Munich, where he studied the interplay between quantum mechanics and general relativity in black-holes.