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