How to use causal AI in your work to make better decisions

Tuesday 10 September 2024 | 18:00 - 19:00 | Webinar

This webinar has been organised by the Data Science Working Group 

Most data scientists know that ‘correlation does not imply causation’. However, traditional data science, machine learning and artificial intelligence (AI) methods can only tell us about correlation, not causation.

In this session, Dimitra (Mimie) Liotsiou, PhD, will show how causal AI, or causal inference, enables data scientists to move from correlation to causation, and to answer questions about causation in a truly transparent, trustworthy and reliable way.

You’ll learn how Causal AI is being used in practice to solve a multitude of high-impact real-world problems, and how you can use it in your day-to-day work.

In this webinar you will:

  • Understand what causal AI is, how it works, and how it enables data scientists to correctly answer cause and effect questions, in finance and in any sector
  • Get up to speed on the trends in causal AI, how it’s used and how it adds value in businesses across sectors
  • See how causal AI can be applied to real problems in Python using open-source libraries and tools.


Registration: 17:55 
Event: 18:00 - 19:00 


Dimitra (Mimie) Liotsiou, PhD

Dimitra (Mimie) Liotsiou, PhD is an experienced data scientist and computer scientist, specialised in causal inference.

Mimie's focus is on developing and using novel computational, data science, and machine learning methods for isolating and measuring causal effects, producing accurate predictions, and developing algorithms, using large real-world datasets.

She has applied her data and computer science work in the domains of retail (dunnhumby London), social media (including online misinformation) and online human interactions (University of Oxford, University of Southampton), healthcare (UK Department of Health, London), and financial computing (Morgan Stanley London).

Mimie's work has been honored with awards and featured in top media outlets.

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