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Biwei Huang: Learning and Using Causal Knowledge: A Further Step Towards a Higher-Level Intelligence

Time: Tue 2022-09-20 16.15

Location: 3418, Lindstedtsvägen 25, and Zoom

Video link: Meeting ID: 621 8808 6001

Participating: Biwei Huang (UC San Diego)


Understanding causal relationships is a fundamental problem in scientific research. Recently, causal analysis has also attracted much interest in computer science and statistics. Accordingly, one focus of this talk is on causal discovery—it aims to identify causal structure and quantitative models of a large set of variables from observational (non-experimental) data, serving as a practical alternative to interventions and randomized experiments. Specifically, I will introduce recent methodological developments of causal discovery in complex environments with distribution shifts and unobserved confounders, together with successful applications. Besides learning causality, another problem of interest is how causality can help understand and advance machine learning and artificial intelligence. I will show what and how we can benefit from causal understanding to facilitate efficient, effective, and interpretable generalizations in transfer-learning tasks.