Recent data mining techniques exploit patterns of statistical independence in multivariate data to make conjectures about cause/effect relationships. These relationships can be used to construct causal graphs, which are sometimes represented by weighted node-link diagrams, with nodes representing variables and combinations of weighted links and/or nodes showing the strength of causal relationships. We present an interactive visualization for causal graphs (ICGs), inspired in part by the Influence Explorer. The key principles of this visualization are as follows: Variables are represented with vertical bars attached to nodes in a graph. Direct manipulation of variables is achieved by sliding a variable value up and down, which reveals causality by producing instantaneous change in causally and/or probabilistically linked variables. This direct manipulation technique gives users the impression they are causally influencing the variables linked to the one they are manipulating. In this context, we demonstrate the subtle distinction between seeing and setting of variable values, and in an extended example, show how this visualization can help a user understand the relationships in a large variable set, and with some intuitions about the domain and a few basic concepts, quickly detect bugs in causal models constructed from these data mining techniques.
Journal or Conference Title
SPIE Proceedings 5669, Visualization and Data Analysis
Jan 16 - Jan 20, 2005
San Jose, CA, USA
Digital Object Identifier (DOI)
Eric M. Neufeld ; Sonje K. Kristtorn ; Qingjuan Guan ; Manon Sanscartier and Colin Ware "Exploring causal influences", Proc. SPIE 5669, Visualization and Data Analysis 2005, 52 (March 17, 2005); doi:10.1117/12.588790; http://dx.doi.org/10.1117/12.588790