Some applications of Causal Inference in the real world — ShareChat ML Seminar
The ShareChat Machine Learning (AI) Seminar invites world-leading researchers to present recent advances in their work in all areas related to machine learning, decision science, and the problems we solve at ShareChat.
Short Bio:
Dr. Ciarán Gilligan-Lee is Head of the Causal Inference research lab at Spotify and an Honorary Associate Professor at University College London. His research has been accepted at top tier ML and AI venues, such as AAAI, NeurIPS, and Nature, and has received global coverage by popular media outlets such as MIT Technology Review, New Scientist, The Times, The Independent, and Newsweek. He holds a DPhil (PhD) from University of Oxford Department of Computer Science.
Causal reasoning is vital for effective reasoning in many domains, from healthcare to economics. 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 and to answer counterfactual queries. In this talk I will present three recent causal inference projects done with my collaborators. One of which is concerned about the ability to estimate long term causal effects when only short-term experiments and long-term observational data affected by the presence of hidden confounders are available. The other two are about how one can learn and reason with counterfactual distributions. In all cases I will strive to motivate and contextualise the results with real word examples.
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