Correlation and causation may look deceptively similar. However, identifying the differences between them can be a make or break in the business, particularly wasting the effort of creating low-quality products and developing products that customers cannot stop raving about. Although both correlation and causation can exist at the same time, correlation does not necessarily imply causation (Rohler, 2018). Causation is applicable only in situations where one action causes another. On the other hand, correlation implies a relationship between two actions, although one action does not necessarily cause the other action (Barrowman, 2014) . The main cause of confusion is the need for humans to create relationships, even where there are none. Often, when two actions are associated closely, people try to force a relationship pattern, which often implies a cause and effect.
It is, however, wrong to assume causation when two events are happening together. This is because the observations of humans are purely anecdotal, and there may be many other possibilities causing the two events to associate. Some other possibilities include;
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The second action causes the first; that is, Action B causes action A.
The two actions are correlated but are caused by another action; that is, Action A and B are correlated, but C causes them.
Another action is involved; that is, action A causes Action B provided E happens.
There exist a chain reaction; that is, action A causes D, which causes B, but only A and B are observable.
Correlation and causation are applied in various fields, such as business and analytics. An example in business is where the organization may need to identify the causation of a product where the behavior of the users causes a specific outcome. Assuming a company develops windows applications when the company launches an update to a certain application, there would need to identify the cause of application retention. The company may formulate a hypothesis that the retention is associated with in-app social behavior. A month after the update, where a new feature was added, only about 30% of the customers have adopted the application. After carrying out a cohort study, it is identified that adoption is increasing. From this, it is clear that most people are using updated information. However, it is not clear whether the new features have caused the adoption; all the information the company has is that the new application is correlated with adoption.
It is, therefore, essential to test causation to identify the cause and effect. A robust analysis is required since associating the two without confirming causation may lead to false conclusions. It is essential to analyze the relationship between the dependent and the independent variable. False-positive are often misleading since they are based on false relationships. For example, the company may associate application retention with additional features, which may cause them to add more features in the future. In contrast, the cause for retention is the simplicity of the application.
Therefore, I think that correlation does not necessarily imply causation in business. When trying to understand the behavior of clients towards a specific product, a thorough analysis is essential to identify the cause and effect. This can be done by testing the formulated hypothesis. According to Barrowman (2014), misunderstanding the situation may cause poor business decisions, which may negatively affect the business in the long-term.
I have experienced the issue of correlation and causation and misjudged it. When streaming HD movies, the quality of the videos was low. This went on for a few days, and I decided to upgrade my plan to accommodate higher quality. I later received an email from my provider, apologizing for internet problems. If I had contacted the provider sooner, I would not have upgraded the plan. I believe this lesson will guide me at the professional level to evaluate all situations before making a decision, which may have negative short-term and long-term effects on my career.
References
Barrowman, N. (2014). Correlation, Causation, and Confusion. The New Atlantis , 23-44. Retrieved from https://www.jstor.org/stable/43551404
Rohler, J. M. (2018). Thinking Clearly About Correlations and Causation: Graphical Causal Models for Observational Data. Advances in Methods and Practices in Psychological Science . doi:10.1177/2515245917745629
