Share Email Print
cover

Proceedings Paper

Exploring causal influences
Author(s): Eric M. Neufeld; Sonje K. Kristtorn; Qingjuan Guan; Manon Sanscartier; Colin Ware
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

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.

Paper Details

Date Published: 11 March 2005
PDF: 11 pages
Proc. SPIE 5669, Visualization and Data Analysis 2005, (11 March 2005); doi: 10.1117/12.588790
Show Author Affiliations
Eric M. Neufeld, Univ. of Saskatchewan (Canada)
Sonje K. Kristtorn, Univ. of Saskatchewan (Canada)
Qingjuan Guan, Univ. of Saskatchewan (Canada)
Manon Sanscartier, Univ. of Saskatchewan (Canada)
Colin Ware, Univ. of New Hampshire (United States)


Published in SPIE Proceedings Vol. 5669:
Visualization and Data Analysis 2005
Robert F. Erbacher; Jonathan C. Roberts; Matti T. Grohn; Katy Borner, Editor(s)

© SPIE. Terms of Use
Back to Top