Mckinsey Interview Questions | Cause vs Correlation
Question
What is root cause analysis? How to identify a cause vs. a correlation? Give examples.
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Answers ( 2 )
Root cause analysis is about going to the depths of the problem and identifying
the real issue which is creating the problem in the first case.
Consulting firms like Mckinsey use methodologies like Issue trees, in which
they start from a high-level problem at hand and start breaking those problems
into smaller issues and understanding the cause behind all those issues.
For Example, a company wants to investigate about the declining profits
from a particular product, they can start from breaking this problem in 2 parts, i.e increasing costs
and decreasing sales, then further scrutinizing the reasons for the occurrence of the both and
continuing the process until they get to the root cause of the problem.
Correlation does not imply causation in every case.
Consider a example, the onset of summer causes an increase in the sale
of ice-creams and sunglasses. If you consider the data points, you will find that
the sale of both the quantities is highly correlated, but increase in the sale of
sunglasses in not the cause of increase in the sale of ice-creams or vice-versa.
The cause for both the cases is the onset of summer.
Correlation is a statistical measure (expressed as a number) that describes the size and direction of a relationship between two or more variables. A correlation between variables, however, does not automatically mean that the change in one variable is the cause of the change in the values of the other variable.
Causation indicates that one event is the result of the occurrence of the other event; i.e. there is a causal relationship between the two events. This is also referred to as cause and effect.