Abstract: Causal analysis, a form of root cause analysis, has been applied to explore causes rather than indications so that the methodology is applicable to identify direct influences of variables. This study focuses on observational data-based causal analysis for factors selection in place of a correlation approach that does not imply causation. The study analyzes the causality relationship between a set of categorical response variables (binary and more than two categories) and a set of explanatory dummy variables by using multivariate joint factor analysis. The paper uses the Minimum Redundancy Maximum Relevance (MRMR) algorithm to identify the causation utilizing data obtained from the National Automotive Sampling System’s Crashworthiness Data System (NASS-CDS) database.

Yawai Tint and Yoshiki Mikami, “A Minimum Redundancy Maximum Relevance-Based Approach for Multivariate Causality Analysis” International Journal of Advanced Computer Science and Applications(IJACSA), 8(9), 2017. http://dx.doi.org/10.14569/IJACSA.2017.080902

@article{Tint2017,
title = {A Minimum Redundancy Maximum Relevance-Based Approach for Multivariate Causality Analysis},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2017.080902},
url = {http://dx.doi.org/10.14569/IJACSA.2017.080902},
year = {2017},
publisher = {The Science and Information Organization},
volume = {8},
number = {9},
author = {Yawai Tint and Yoshiki Mikami}
}