Research

Statistical Methodology for Network Analysis

As part of our Program Project grant on "Networks and Neighborhoods," we are actively seeking to develop new statistical methods that allow for more robust causal inference with complex network data. The work focuses on exploiting the longitudinal change in network topology and in nodal covariates to better identify causal effects, and on developing instrumental variable methods for network applications. In addition, we are developing methods of missing data imputation in networks. This work involves numerous datasets, including the FHS-Net, our Facebook dataset, and others.

The team of faculty investigators working on this topic include:  James O'Malley, Yulei He, Alan Zaslavksy, Peter Marsden, James Fowler, Joe Blitzstein, and Neils Rosenquist.

© 2008 Nicholas Christakis