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Rothschild Lecture: Causality, Invariance and Robustness

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STS - Statistical scalability

Is it a cause or an effect? This simple but fundamental question has a long history in science and society. Randomised studies serve as the gold standard for inferring causality but they are often expensive or even
impossible to do due to ethical reasons. Recent approaches try to “substitute in part” the randomised studies by models, algorithms and statistical methods. Perhaps surprisingly, heterogeneity in potentially
large-scale data can be beneficially exploited for causal inference and novel robustness, with wide-ranging prospects for various applications. The key idea relies on a notion of probabilistic invariance: it opens up new insights with connections to frameworks used in robust optimisation and economics.

This talk is part of the Isaac Newton Institute Seminar Series series.

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