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Hidden Confounding: How Time and Experiments Help

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CIFW01 - Foundations of causal inference

Causal models help us predict how a real-world system reacts underactive perturbations. In many applications, however, causal modelscannot be derived from first principles and instead need to be learnedfrom data. This is particularly challenging if the system exhibitshidden confounding, i.e., some of the causally relevant variables areunobserved. In this talk, we discuss strategies for dealing with hiddenconfounding under suitable assumptions, such as access to time-dependentdata or observations collected under different experimental conditions.

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

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Zoom: https://zoom.us/j/834693107 Meeting ID: 834 693 107 Online via zoom & Kings Hedges Room CUED LR5

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