Real life is complicated and messy. If you encounter a root cause analysis where the data fits the solution too neatly, be careful. It is rare that all evidence fits a theory, it if does, there's a chance that something was left out in order to make it fit or the data was handled incorrectly. This is known as 'silent evidence' since it is not presented or available. This is another form of confirmation bias, the situation where we only focus on data that fits our mind-set or theory.
For an example of this see, the "story" presented by a correlation plot of time spent eating versus Body Mass Index (BMI) for a number of countries presented on the NY Times website. Read the comments and you can see that there are many flaws in the story illustrated by the x-y scatterplot. The plot weaves a nice story supporting the author's prejudice about fast food but the data does not support that story. (nor does it not support it)
A strength of the Apollo root cause analysis approach is that it helps force you to consider many causes that contribute. However, the trap we fall into is to focus on only one cause-effect path and neglect others. This may make a nice story but won't necessarily reflect reality.