Modern Bayesian Statistics in Clinical Research pp 131142  Cite as
Bayes and Causal Relationships
Abstract
The current search for causal relationships with Bayesian structural equation modelings will be addressed in this chapter as an example where Bayesian methodologies successfully helped fostering the deepest enigma of mankind, the proof of causality.

latent factors (unmeasured variables inferred from measured variables) as predictors, we will call it:

factor analysis if unsupervised (i.e., no dependent variable);

partial least squares if supervised;

discriminant analysis if like the above two but including a grouping variable.

manifest factors (variables), we will call it:

multistage regressions which might include flawed predictors, given pejorative names, like problematic predictors, multicollinear predictors, indirect predictors.
Bayesian networks do not necessarily use traditional Bayesian statistics, i.e., posterior and prior odds. Instead they may use the methodologies of more modern Bayesian statistics, i.e., conditional and marginal probability distributions (running from 0 to 1), and likelihood distributions (even running from 0 to ∞).
Suggested Reading^{1}^{,}^{2}
 Statistics applied to clinical studies 5th edition, 2012,Google Scholar
 Machine learning in medicine a complete overview, 2015,Google Scholar
 SPSS for starters and 2nd levelers 2nd edition, 2015,Google Scholar
 Clinical data analysis on a pocket calculator 2nd edition, 2016,Google Scholar
 Understanding clinical data analysis from published research, 2016,Google Scholar
 Modern Metaanalysis, 2017,Google Scholar
 Regression Analysis in Clinical Research, 2018.Google Scholar