Cause Verification is the step in problem solving where the team tests whether a suspected cause actually changes the observed problem.
A plausible cause is not yet a verified cause.
The team should be able to answer:
What evidence shows that this cause produces the effect?
Distinguish correlation from causation
A condition may occur near the problem without creating it.
For example:
- a new operator started;
- a material lot changed;
- maintenance occurred;
- temperature increased.
These are useful clues.
They are not proof.
Change Point Analysis helps identify changes worth investigating when a problem appeared or worsened.
Cause Verification determines whether the suspected change can actually explain the effect.
Define the causal mechanism
Ask:
- How would this cause physically or logically create the problem?
- What intermediate condition should we observe?
- What evidence would contradict the theory?
A causal mechanism makes the hypothesis testable.
Without it, the team may simply collect supporting anecdotes.
Compare good and bad conditions
Look for whether the suspected cause is present when the problem is present and absent when the problem is absent.
Is/Is Not Analysis helps compare conditions where the problem occurs with similar conditions where it does not.
This can strengthen or weaken the suspected cause.
Test safely where practical
A controlled test may:
- restore the previous condition;
- change one factor;
- reproduce the suspected condition;
- compare two settings.
Small Tests of Change provide a disciplined way to test a hypothesis with limited risk.
The test should protect safety, quality, and customer requirements.
Use data when direct testing is not possible
Sometimes it is unsafe or impractical to recreate the suspected cause.
Alternative evidence may include:
- historical records;
- natural experiments;
- trend comparison;
- statistical analysis;
- physical inspection.
The evidence should still test whether the suspected cause consistently explains the effect.
Look for repeatability
A strong verified cause should produce a repeatable relationship.
If the problem appears only occasionally when the suspected cause is present, another condition may also be required.
This can reveal:
- interaction;
- threshold;
- combined causes.
Design of Experiments may be useful when several factors interact and simpler comparison cannot isolate the effect.
Reject causes that fail the test
Teams sometimes become attached to their first theory.
A failed verification should remove or weaken that cause from the analysis.
The purpose is not to prove the team was right.
The purpose is to identify what actually changes the problem.
Connect verified cause to countermeasure
A countermeasure should address the verified mechanism.
Countermeasure Management helps connect actions, expected results, ownership, and follow-up after the cause has been established.
Without cause verification, the countermeasure may only treat a symptom.
Common mistakes
Treating correlation as proof, accepting expert opinion without testing, changing several factors at once, using only evidence that supports the preferred theory, refusing to discard a failed hypothesis, and implementing countermeasures before verifying the causal mechanism are common mistakes.
Practical sequence
- define the suspected cause.
- describe the causal mechanism.
- identify evidence that should exist if the cause is real.
- compare good and bad conditions.
- design a safe test where practical.
- change as few variables as possible.
- observe the result.
- repeat or confirm with independent evidence.
- reject unsupported causes.
- design the countermeasure around the verified mechanism.
The practical lesson
Cause Verification turns a theory into evidence.
A root cause should be demonstrated strongly enough that changing it predictably changes the problem.
Related application
This topic also connects with Hypothesis-Driven Problem Solving. Use that method when the improvement requires the related operating or management discipline.