University of Cambridge > > Machine Learning @ CUED > Identification of causal effects

Identification of causal effects

Add to your list(s) Download to your calendar using vCal

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.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.


© 2006-2024, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity