Testing Cause Relationships and Root Depth
At a manufacturing plant last quarter, a recurring machine failure kept reappearing after a fix was deemed complete. The team thought they’d resolved it—until it failed again. We revisited the Fishbone diagram and realized the fix only addressed a surface-level symptom, not the deeper systemic flaw. This happens far too often: teams stop at the first “root” they identify, mistaking a strong contributor for the true root cause.
Root cause correlation isn’t just about listing causes. It’s about understanding how they connect, how one leads to another, and where the real leverage lies. In my two decades of leading RCA teams, I’ve found that the most persistent failures stem not from single failures but from cascades of interdependent issues—what I call “multi layer cause mapping.”
This chapter shows how to go beyond the Fishbone’s surface to test cause relationships, challenge assumptions, and dig deep into systems. You’ll learn to distinguish between contributing factors and true root causes, apply structured logic to trace causal chains, and validate depth using evidence—not opinion. By the end, you’ll know when a cause has been tested sufficiently and when it’s time to dig deeper.
Unpacking Cause Relationships: From Isolation to Interconnection
When you first list causes in a Fishbone diagram, they often appear isolated. But in reality, most problems stem from dependencies—what one cause triggers, another enables, and a third sustains.
Consider a software deployment failure. A missing environment variable might be listed as a cause. But why was it missing? Because the configuration script didn’t run. Why didn’t it run? Because the deployment pipeline skipped the step due to a misconfigured trigger. Why was the trigger misconfigured? Because the team wasn’t trained on the new rollout process.
These aren’t separate causes—they’re linked in a chain. One failure enables another. That’s why cause relationship analysis is essential: it reveals the hidden structure behind seemingly disjointed events.
Use the “Why-Why” Chain to Test Causal Logic
After listing initial causes, apply a targeted version of the 5 Whys—not to every cause, but to those that feel questionable or incomplete. The goal is to test whether the cause you’ve identified is truly causal or just a symptom.
Start with the cause: “The server crashed due to memory overflow.” Ask: Why? Because the application consumed too much RAM. Why? Because a memory leak was present in the code. Why? Because the developer failed to release a reference object. Why? Because the code review didn’t catch it.
Now you’ve moved from a technical symptom to a process gap. The root isn’t the memory leak—it’s the lack of effective peer review protocols. This is where true root depth emerges.
Use this rule: if you can’t answer “why” beyond two or three layers, you’re likely still at the symptom level. If you reach an organizational or procedural gap, you’ve reached the real root.
Map Causes with Dependency Chains
Don’t assume the Fishbone captures the full picture. Supplement it with a dependency map—draw lines between causes showing which enables or triggers another.
For example, if “lack of training” appears in the “People” category and “no documentation” in “Method,” place a line from “no documentation” to “lack of training” and label it “causes.” Then ask: does this dependency hold? Is training actually impossible without documentation? Yes—so the gap isn’t just in training. It’s in knowledge transfer.
Use this format:
- Trigger Cause: No documentation exists
- Enabling Cause: Lack of training
- Effect: Team makes incorrect configuration decisions
Now you see the pattern: one failure feeds the next. This is multi layer cause mapping in practice.
Validating Root Depth: The 3-Tier Test
Not all causes are equal. Some are symptoms. Some are contributing factors. Only a few are true root causes. To avoid settling too early, apply the 3-Tier Test to any candidate root cause.
Tier 1: Is it a measurable, physical, or observable event?
Yes → move to Tier 2.
No → It’s likely a symptom or a judgment. Revisit the evidence.
Tier 2: Can this cause be eliminated without changing the system?
Yes → It’s a contributor, not a root. The system will still fail in the same way.
No → Proceed to Tier 3.
Tier 3: Is this cause tied to a process, policy, or systemic pattern?
Yes → You’ve found a root cause.
No → It’s still a contributor. Dig deeper.
I once worked with a hospital where a patient received the wrong medication. The initial Fishbone listed “nurse misread label” as a cause. Tier 1? Yes, observable. Tier 2? No—eliminating the nurse wouldn’t fix the problem, because another nurse might make the same mistake. Tier 3? Only after uncovering the real root: the labeling system wasn’t standardized across departments, and no override process existed for ambiguous labels.
That’s underlying cause detection in action. The real issue wasn’t human error—it was a design flaw in the process.
Common Pitfalls in Cause Depth Assessment
Even experienced teams fall into traps when testing root depth. Be aware of these:
- Blaming individuals: If the root cause centers on “Bob forgot to check,” dig deeper. What system failed to remind him? Was he overloaded? Was the process unclear?
- Stopping at the “first” root: A single root cause is rare. Most problems have multiple systemic roots. Look for patterns across incidents.
- Confusing “why” with “how”: “How” explains mechanics. “Why” reveals intent and structure. Ask “why” to uncover system gaps.
- Accepting “no data” as a reason to stop: If evidence is missing, ask: “What would be required to confirm or deny this cause?” Then plan to collect it.
Tools and Techniques for Deeper Investigation
When you suspect a deeper issue, use structured methods to test your assumptions.
Checklist: When to Keep Digging
- The cause is tied to a person’s behavior. Ask: What system influenced their decision?
- The same issue recurs after correction. Ask: Why did the fix not prevent recurrence?
- Multiple teams report similar problems. Ask: Is this a systemic failure?
- Root cause feels vague (“poor communication”). Ask: What specific communication breakdown occurred?
Visual Aid: The Cause Depth Matrix
This table helps classify causes by depth and impact.
| Depth Level | Example | Impact | Intervention Type |
|---|---|---|---|
| Symptom | Server crashed | High (immediate) | Short-term fix |
| Contributing Factor | Memory leak in code | Medium | Technical fix |
| Process Gap | No peer review for deployment scripts | High (long-term) | Systemic change |
| Systemic Root | No standardized deployment protocol across teams | Very High | Policy & culture change |
Use this matrix during your RCA session to evaluate each candidate cause. The goal is to target interventions at the process or systemic level—where lasting change happens.
Frequently Asked Questions
How do I know when I’ve reached the true root cause?
When your cause cannot be eliminated without changing a process, policy, or system. If removing it doesn’t prevent future recurrence, it’s not the root. The true root cause is one that, if fixed, would stop the problem from reoccurring under the same conditions.
Can a problem have more than one root cause?
Absolutely. In complex systems, multiple root causes often interact. For example, a software outage may be due to both a flawed deployment process and insufficient monitoring. Treat each root separately, but map their interplay to understand the full impact.
What if the team disagrees on the root depth?
Facilitate a structured discussion using the 3-Tier Test. Ask each member to justify their view with evidence. If consensus is still elusive, assign a follow-up task to collect data—then reassess. Disagreement is a sign of complexity, not failure.
How do I avoid overcomplicating cause mapping?
Start simple. Use the Fishbone to capture top-level causes, then apply the “Why-Why” chain only to candidates with potential depth. Focus on the 2–3 most likely root causes. Avoid mapping every possible interaction—unless data shows they’re critical.
Is multi layer cause mapping only for complex systems?
No. Even simple problems can hide layers. A single machine failure might seem trivial, but beneath it could lie a pattern of maintenance neglect, lack of documentation, or poor supplier quality. Always test depth, even for small issues.
How often should I revisit root cause detection in ongoing processes?
After every corrective action, run a mini-review. Ask: “Has this fix prevented recurrence?” If not, re-examine the root cause. Treat root cause analysis not as a one-off but as part of a continuous learning cycle.
