This project aims to enable the method of Path Analysis to infer causalities
from data. For this we propose a hybrid approach, which uses Bayesian network
structure learning algorithms from data to create the input file for creation of a
PA model. The process is performed in a semi-automatic way by our intermediate
algorithm, allowing novice researchers to create and evaluate their own PA models
from a data set. The references used for this project are:
Koller, D., & Friedman, N. (2009). Probabilistic graphical models: principles and techniques. MIT press. .
Nagarajan, R., Scutari, M., & Lbre, S. (2013). Bayesian networks in r. Springer, 122, 125-127. Scutari, M., & Denis, J. B. .
Scutari M (2010). Bayesian networks: with examples in R. Chapman and Hall/CRC. .
Rosseel, Y. (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48(2), 1 - 36. .