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Are our DAGs correct? Recent developments in causal discovery evaluation

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CIFW02 - Causal identification and discovery

Causal graphical models are considered important tools to integrate expert knowledge into statistical data analysis. As consequence, practitioners often face the challenge to evaluate the quality of their hypothesized causal models. This issue becomes particularly salient for causal graphical models obtained through a causal discovery method in which case most or all of the available data has already been used in the discovery task. In this talk, we will review recent developments regarding the evaluation of causal structure learning methods and the quality of their outputs. In particular, we will discuss to which degree assumption violations can be detected in causal discovery and how method testing often introduces additional assumptions that need to be weighted carefully against the assumptions of the initial learning algorithm.

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

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Department of Earth Sciences, Wolfson Lecture Theatre, Bullard Rooms SG1 & SG2, Alison Richard Building, 7 West Road, Cambridge, CB3 9DT Clinical School, Seminar Room 12

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