Causalities between data points are represented as directions in a graph. It can play an important role in explanation, prediction, interpretation, and decision making. Also, since several studies have shown that the algorithms are improved when using the directed graph, causal inference between data points may contribute to improving the prediction performance of the algorithms. The purpose of this study is to infer causalities with a high possibility through learning and extracting from data among a huge number of possible causalities.
The proposed methods consist of two approaches: (a) machine learning- and (b) data mining-based causal inference on graphs. For the machine learning model, ELFNet, an Edge Labeling and Feature inference neural network is proposed. It predicts the directionality between nodes with edge labeling and infers the edge features simultaneously. For the data mining model, αDCFC, extracts causality between elements from text data and quantify the degree of the causal relation is proposed. The proposed methods provide insight to understand how data points are related to each other.
Causal inference also can be used in a variety of analyzes by using the results. Therefore, applications that can be applied to a disease network when causal information is provided are presented in two aspects: (a) the shortest path search algorithm for finding causal disease chains and (b) the machine learning algorithm for disease comorbidity scoring. The results of applications demonstrate that causal information provides extended insight or improving algorithms.
