An Emerging Solution to Harmonize Various Causal Discovery Methods

Presented by

Nima Safaei

About this talk

The Causal AI Conference 2022: An Emerging Solution to Harmonize Various Causal Discovery Methods Nima Safaei Senior Data Scientist, Scotia Bank Explainability is one of the most desired properties of AI systems; without which the AI systems cannot be trusted in high-risk fields. Causal Inference (CI) is a vital tool for producing more insightful explainability. However, one major shortfall in the current CI literature is the lack of a unique definition for causality; resulting in many different methods such as pairwise dependency tests, statistical conditional tests, structural models, and graph-based models. One major barrier to the use of CI for explainability in AI applications is that the various CI methods usually result in different causal graphs with different inter-connectedness and density; specifically given a high-dimension feature space. In this talk, Nima will address this challenge from the perspective of the financial services industry, walk through the associated complexities, and outline the possible methods to find a solution that harmonizes various CI methods. Learn more and join the community here: 00:00 - Welcome 00:16 - An Emerging Solution to Harmonize Various Causal Discovery Methods 25:23 - Q&A

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