Abstract
D.Phil. (Electrical and Electronic Engineering)
The phenomenon of cause and effect which rules the natural behaviour of the universe is
simple in observation but complicated in interdependency. While all action and reaction
states observed in time space are easier to work on, still the difficulty lies in the factor
relations. Only knowing the facts/features without the time frame as they occurred/observed
heightens the complexity of information retrieval. The relation of cause and effect is vital
for knowing the past information which constructs the present state, although feature links
remain debatable in this case. The study of Causality deals with these exploratory data
analysis problems to inform all possible vital facts which can be extracted from the feature
sets. Many researchers also consider the Causal Analysis as the golden standard in data
mining and analysis.
As is frequently the case, this causal analysis is represented by directed acyclic graphs
for simplification of complexity. The directed edges with weighted values inform the flow
of information from source/parent to the receiver/child nodes in the graph. The definition
of the causal structure for inference analysis is insufficient for many reasons, and the works
concluded in this field are inadequate. Most of the techniques proposed provide limited
structural analysis, while many others are not able to validate the required criteria for causal
analysis. The background study of all the proposed articles with definite contributions towards
causality have been studied and are thoroughly analyzed in the literature review.
All the methods proposed yet, use the bivariate model for causal analysis. In this scenario,
the model, Linear Non-Gaussian Acyclic Model (LiNGAM) is the first to provide estimation
for the most number of features. However, it is not completely effective in analyzing the
causal models for datasets of mixed distribution types and also constructing a complete causal
model from the estimated results is not possible. While using the fundamental structure of
LiNGAM, the estimation process for causal detection is newly introduced by the method
Altered-LiNGAM (ALiNGAM) in this work. ALiNGAM uses least square estimation on dseparable
sets to find the probable causal directions in the observed feature set. The proposed...