Causal AI is a new category of intelligent machines designed to reason about the world like humans (according to AI visionaries):
- “To build truly intelligent machines, teach them cause and effect.” Judea Pearl, Turing Award winner 2011, Author 'Book of Why?', 2018
- “Causality is very important for the next steps of progress of machine learning.” Yoshua Bengio, Turing Award winner 2018, considered one of 'the godfathers of Deep Learning'
- Nobel prize recently awarded for "analysis of causal relationships" The Sveriges Riksbank Prize in Economic Sciences in memory of Alfred Nobel, awarded to Professors Joshua Angrist and Guido Imbens in November 2021
- What we mean by Causal AI
- Foundational concepts in Causal AI
- Current state & existing approaches to Causal AI modeling
- Examples in asset management that can be viewed differently through a causal lens:
- portfolio optimisation,
- discovery of orthogonal model factors, and
- evaluation of strategies via causal risk assessment
Ben Steiner helps asset managers and institutional investors deploy causaLens technology to add value in their investment processes. His background includes Quantitative Researcher, Portfolio Manager, Head of Model Development and Chief-of-Staff roles at asset managers and quantitative hedge funds. He serves on the board of directors of the Society of Quantitative Analysts (SQA) and teaches an applied modeling course at Columbia University.
Location and time
This event will be hosted online. 31 March, 16:00 - 17:00
Professional Learning Credits