Centre for Doctoral Training in AI-enabled Healthcare


Causal Inference in Healthcare

03 April 2020, 12:30 pm–1:30 pm

Join us on Friday 3rd April, 12.30-13.30 as we welcome Dr. Ciarán M. Lee (Senior Researcher at Babylon Health) speak about Causal Inference in Healthcare.

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Craig Smith

Causal reasoning is vital for effective reasoning in science and medicine. In medical diagnosis, for example, a doctor aims to explain a patient’s symptoms by determining the diseases causing them. This is because causal relations---unlike correlations---allow one to reason about the consequences of possible treatments. However, all previous approaches to machine-learning assisted diagnosis, including deep learning and model-based Bayesian approaches, learn by association and do not distinguish correlation from causation. I will show that these approaches systematically lead to incorrect diagnoses. I will outline a new diagnostic algorithm, based on counterfactual inference, which captures the causal aspect of diagnosis overlooked by previous approaches and overcomes these issues. I will additionally describe recent algorithms from my group which can discover causal relations from uncontrolled observational data and show how these can be applied to facilitate effective reasoning in medical settings such as deciding how to treat certain diseases.

About the Speaker

Dr. Ciarán M. Lee

Senior Researcher at Babylon Health

Dr. Ciarán M. Lee is a Senior Researcher at Babylon Health, where he leads the Causal Inference team, and an Honorary Senior Research Associate at UCL. His work has been presented at some of the top AI conferences, such as AAAI and NeurIPS, and has been covered by popular media outlets such as MIT Technology Review, New Scientist, and Gizmodo. Before joining Babylon Health he was PI of an EPSRC grant at UCL on causal inference and its applications. He received his PhD (DPhil) from University of Oxford Department of Computer Science.