Marco Zaffalon:
Casual Inference is Imprecise Probability in Action

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Causal inference is a beautiful recent theory with a wealth of important applications waiting to be pursued. It is also a native theory of imprecise probability; there is so much to be gained in creating bridges across the two fields. In this talk, I will introduce causal inference, give examples of methods and algorithms that can help you cross the bridges, and thus start right away to apply causal inference in your domain.

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Marco Zaffalon is a University Professor and Scientific Director at IDSIA. He has 160 refereed publications on artificial intelligence and machine learning, and his research has been supported by 15 million francs in competitive grants. Marco is a Senior Area Editor of the International Journal of Approximate Reasoning and has been a past President of the Society for Imprecise Probability. At IDSIA he has founded and leads the group on probabilistic machine learning that is made of 40 researchers. In his applied research he has worked with UBS, Novartis, Mastercard, and several other companies. In 2020 he has co-founded Artificialy, a company for products and services in AI, where he works as the company's Chief Scientist.