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Identification of causal effects

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If you have a question about this talk, please contact Konstantina Palla .

Establishing cause-effect relationships from a combination of data and assumptions is a fundamental part of empirical science. Graphical models provide a useful framework for representing assumptions about the world and formalizing causal inference. In this talk, I will first describe a complete algorithm by Tian & Pearl for determining whether a causal effect is identifiable from observational data for a given graphical model. I will then discuss the relationship between identifiable effects and recursive factorization of the observational distribution, with potential implications for computationally efficient inference.

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

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