Root cause analysis vs causal modeling: when the five whys are not enough
Root cause analysis traces the causes of one event back through a chain of whys. Causal modeling maps how the whole system behaves and answers what to change to prevent the next one.
5 min read
What root cause analysis does well
Techniques such as the five whys and fishbone diagrams work backward from a failure to its causes. They are fast, need no data and force a team to move past the first symptom. For investigating a specific incident they are hard to beat.
Where it runs out
Recurring problems rarely have a single root. Several factors interact, some sit on the same path as others, and a fix aimed at one may do nothing because another is doing the work. The five whys follows one chain and stops, and different teams following the same method often reach different roots.
It is also backward-looking. It explains what happened, but it does not compare candidate fixes, and it leaves no record of how confident anyone was in each link.
What a causal model adds
A causal model keeps the whole structure in one place, which supports different questions.
- Which of several candidate fixes is expected to move the outcome most?
- Which factors are shared causes that would mislead a simple comparison?
- Can this question be answered at all from what we know?
- How far could the answer move if our assumptions are wrong?
How to combine them
Use root cause analysis to investigate the incident and generate hypotheses. Then turn those hypotheses into a causal graph, mark what is measured and what is assumed, and ask which intervention is worth funding. The investigation feeds the model, and the model turns the findings into a decision that can be defended and revisited.
Written by the AnantCausal team. More in the guides library.
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