the three model scores in a single probability value $P(A\to B)$ for a causal interaction from gene $A$ to $B$. This method is available in the **Trigger** software (<https://www.bioconductor.org/packages/release/bioc/html/trigger.html>). In [@millstein2009disentangling] and [@neto2013modeling], the model selection task is recast into a single hypothesis test, using $F$-tests and Vuong’s model selection test respectively, resulting in a significance $p$-value for each gene-gene causal interaction.

It should be noted that all of the above approaches suffer from limitations due to their inherent model assumptions. In particular, the presence of unequal levels of measurement noise among genes, or of hidden regulatory factors causing additional correlation among genes, can confuse causal inference. For example, excessive error level in the expression data of gene $A$, may mistake the true structure $E\rightarrow A\rightarrow B$ as $E\rightarrow B\rightarrow A$. These limitations are discussed in [@rockman2008reverse; @li2010critical].

Using Bayesian networks to identify causal regulatory mechanisms
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Bayesian networks are probabilistic graphical models which encode conditional dependencies between random variables in a directed acyclic graph (DAG). Although Bayesian network cannot fully reflect certain pathways in gene regulation, such as self-regulation or feedback loops, they still serve as a popular method for modelling gene regulation networks, as they provide a clear 