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Causal Inference

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If you have a question about this talk, please contact Rowan McAllister .

In this talk, we present the main challenges and developments of causal inference. By motivating why the language of statistics is insufficient to talk about causality, we introduce the necessary tools to discuss interventions. We also look into methods for recovering a DAG given observational data from a joint distribution, as well as commonly used techniques such as instrumental variables.

For a very good overview see: Causal inference in statistics: An overview

This talk is part of the Machine Learning Reading Group @ CUED series.

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