Distinguished Lecture in Data Science: Just How Doomed is Causal Inference for Social Networks, Exactly?

Date and Location

May 06, 2019 - 4:00pm to 5:00pm
Loma Pelona 1108


Cosma Shalizi
Department of Statistics and of Machine Learning
Carnegie Mellon University


People near each other in a social network tend to act similarly; you can predict what one of them will do from seeing what their neighbors do.  Is this because they are influenced by their neighbors' actions, or because social ties tend to form between people who are already similar, and so act alike, or some of both?  We show that observational data generally can't answer this question, unless accompanied by very strong assumptions, like measuring everything that leads people to form social ties.  Most observational studies thus provide no evidence at all about the existence or strength of social influence.  There are, however, some situations where the global configuration of the social network can tell us enough about its individual nodes to get around this.

Cosma Shalizi is an associate professor in the departments of statistics and of machine learning at Carnegie Mellon University.  He has a Ph.D. in physics from the University of Wisconsin-Madison, and was a post-doc at the University of Michigan and the Santa Fe Institute, where he is now on the external faculty.  He still blogs sporadically at http://bactra.org/weblog/.

This talk is a Distinguished Lecture in Data Science, sponsored by Amazon, Graphiq, and the Department of Statistics and Applied Probability.