Abstract—Representing causal relation between set of
variables is a challenged objective. Causal Bayesian Networks
has been classified as good modeling technique for this purpose.
However structure learning for causal Bayesian networks still
suffering from several problems including the causal
interpretation of the model and the complexity of the learning
algorithm. In this research the author presents an approach for
learning causal graph based on Wiener-Granger causal-theory,
with minor modifications, and use Genetic Programming to
determine the parameters of Granger formula. This approach
enjoys necessary advantages: reasonable complexity and cover
nonlinear equation. A case study of 5 global stock markets is
presented to experimentally explain and support this approach.
The finding show that SP500 has Granger-causal influence on
NIKKE: the accuracy of forecasting NIKKE stock market can
be incremented by 24% when integrating past data from
SP500. Whereas Euro STOXX 50 is reported to be the least
stock Granger-causally affected by the others.
Index Terms—Genetic programming, granger-causality, learning causal graph, stock market forecasting, JEL classification: G15 – C32 – D83.
Amer Bakhach and Mahmoud Samad are with the Computer Sciences Department, Lebanese International University, Lebanon (e-mail: email@example.com, Mahmoud.firstname.lastname@example.org).
Cite: Amer Bakhach and Mahmoud Samad, "Learning Causal Graph: A Genetic Programming Approach," International Journal of Machine Learning and Computing vol.4, no. 3, pp. 243-249, 2014.