(Georgia Institute of Technology, 2010-12-20)
Ebert-Uphoff, Imme; Deng, Yi
This paper suggests new methods for the development of network models in
climate research. Current climate networks, first introduced in 2004 by Tsonis and Roebber, define network edges based on correlation of node
pairs, resulting in a correlation network. The key idea of this paper
is to introduce techniques from causal reasoning to derive climate
networks, specifically constraint based structure learning. This
approach is expected to yield networks that better represent the causal
connections in the network, by containing less edges and with all causal
pathways still present. The anticipated advantage of a network with
less edges is a more manageable model size that makes it easier to gain
new insights about causal relationships in the climate system.
The goal of this paper is to provide researchers in the climate area
with an intuitive understanding of the causal discovery process,
specifically of constraint based structure learning. We review the
basic principles of constraint based structure learning, namely how
cause-and-effect relationships of variables can be learned from
observational data using conditional independence tests. Tutorial-style
examples illustrate this process. Finally, we review available
algorithms and software packages
from other disciplines that can be applied to derive climate networks.
There are no simulation results provided in this paper (work in
progress), thus we do not yet
know how much reduction is achieved through this method compared to
existing methods. However, applications of similar techniques for
protein interaction modeling has yielded tremendous savings, making it
possible to gain significant understanding of causal pathways from the
obtained network graphs.