Machine Learning - Special issue on learning with probabilistic representations
A new Bayesian tree learning method with reduced time and space complexity
Fundamenta Informaticae
Applying Two-Level Simulated Annealing on Bayesian Structure Learning to Infer Genetic Networks
CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
A causal mapping approach to constructing Bayesian networks
Decision Support Systems
TAN Classifiers Based on Decomposable Distributions
Machine Learning
Computational Statistics & Data Analysis
A causal analytical method for group decision-making under fuzzy environment
Expert Systems with Applications: An International Journal
Towards efficient variables ordering for Bayesian networks classifier
Data & Knowledge Engineering
Computational Statistics & Data Analysis
Software maintenance project delays prediction using Bayesian Networks
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Auto claim fraud detection using Bayesian learning neural networks
Expert Systems with Applications: An International Journal
Model gene network by semi-fixed Bayesian network
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Exploiting causal independence in large Bayesian networks
Knowledge-Based Systems
Learning tree augmented naive bayes for ranking
DASFAA'05 Proceedings of the 10th international conference on Database Systems for Advanced Applications
Approximating discrete probability distributions with dependence trees
IEEE Transactions on Information Theory
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part II
Hi-index | 12.05 |
Causal knowledge based on causal analysis can advance the quality of decision-making and thereby facilitate a process of transforming strategic objectives into effective actions. Several creditable studies have emphasized the usefulness of causal analysis techniques. Partial least squares (PLS) path modeling is one of several popular causal analysis techniques. However, one difficulty often faced when we commence research is that the causal direction is unknown due to the lack of background knowledge. To solve this difficulty, this paper proposes a method that links the Bayesian network and PLS path modeling for causal analysis. An empirical study is presented to illustrate the application of the proposed method. Based on the findings of this study, conclusions and implications for management are discussed.