Indepth Analysis into Correlation and Causation

Business data analytics, more commonly called business analytics, is a process of data analytics dedicated explicitly to glean key business insights from volumes of collected data using pre-established business tools and content. Simply put, business analytics analyses data collected from all walks of an enterprise to identify key business insights such as causes and trends to facilitate a data-driven decision-making process for the business. Therefore, it is no surprise that business analytics is an essential specialisation that is key to smooth and efficient business growth.

If you are familiar with even the basics of business data analytics, you might have heard of the correlation vs causation debate. It is a long-standing problem that many young and even experienced data scientists are faced with. 

This article provides an in-depth analysis of the difference between correlation and causation with examples. We also talk about the possibilities of a career in business analytics and how to get yourself started. So, read on!

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How Are Correlation And Causation Analysed? 

To go into the depths of correlation vs causation, it is first important to understand what they are. 

Correlation can be understood as a number representing the relationship between two or more variables. This statistical measure is used to understand how a particular target variable is dependent on another independent variable. On the other hand, causation points towards a causal relationship between two variables. In other words, causation indicates that the change in a variable results from a change in another variable. 

The most widely used method to calculate a correlation between two or more linearly related variables is the Pearson r correlation which renders three possible outcomes:

• Positive correlation where two variables simultaneously increase.
• Negative correlation where two variables simultaneously decrease.
• There is no correlation where a change in one variable does not see a change in the other.

Two processes can establish causation after correlation:

• Controlled study – In this method, the variables and data are divided into two groups: interest, the dependent variable, and treatment, the independent variable. Different experimentation is performed on the variables, keeping the groups comparable in every possible way. The outcomes are carefully and statistically assessed to arrive at a conclusion about causation.
• Non-spuriousness – This is an elimination method where data scientists make great efforts to rule out all possibilities of a spurious or a false relationship where variables A & B show a correlation but because of a third variable, C.

It is now widely accepted that even if a specific correlation is established between two or more variables, the correlation coefficient thus obtained must not be used to conclude a cause-effect relationship between the variables. When two variables show a relationship that indicates a correlation, it is perhaps safe to anticipate the existence of causality. However, a definitive conclusion of this does not happen. This is the basis for understanding the difference between correlation and causation. 

Key Difference Between Correlation And Causation

Humans tend to find patterns to make sense of the things around them. Even if patterns don’t exist and two events are unrelated in reality. This is why we often tend to confuse correlation vs causation and assume a causal effect to any correlation. The key difference between correlation and causation stems from the basic concept that if a correlation is established between two variables, we cannot necessarily conclude that one variable causes any change in the other variable. 

If a causal relationship is established, analysts can manipulate one variable to achieve the desired outcome in the dependent variable. However, if there is only a correlation between two variables, then there is no guarantee that any change to one variable will change the other variable. Let us look at some correlation vs causation examples that will illustrate the difference between correlation and causation for you:

• A brand’s marketing department starts to actively run an Instagram page, posting company updates, vision statements, tips and tricks and product promotions. In a few weeks, the sales of a particular product grow. So we now have a definitive correlation between the number of posts on Instagram and the product’s sales.
  However, this does not indicate a causal relationship between the two events. Business analysts have to consider multiple other factors such as product-specific promotional campaigns, market prices, demography of the customers, etc., before they draw a conclusion to causation.
• A brand makes significant updates to the UI of their app, and in a few weeks, the app has more ratings in the app store. Thus, a correlation is established. However, this is not enough to imply causation.
• A business analyst must consider various other factors such as UX, demography of the customers etc. and possibly even do a controlled trial with a select group of customers to establish a causal relationship.

A thorough analysis of correlation vs causation is crucial for companies to make crucial business decisions based on specific data insights. Conversely, decisions taken based on correlation findings can often be counter-productive. For a business analyst in a company, large or small, it is essential to arrive at a definitive causal relationship before relaying insights to the decision making authorities. This often proves to be a significant make or break in company growth.

A Career In Business Analytics

Business Analytics has seen phenomenal growth in all aspects of a business, from social media, marketing, sales, finance, eCommerce, human resource management, warehousing, etc. Modern business analytics is Big Data, AI and ML-powered, housing various data visualisation and data analysis tools under its umbrella. Thus, as business analytics’s impact and complexity grow, so does the demand for skilled talent in this niche. Many data analysts and data scientists gravitate towards business analytics because of the exciting prospects.

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Conclusion

A career in business analytics has long-term prospects for stability and high salaries. Moreover, the increasing dependence of businesses on innovative technology makes any data-driven career dynamic and evolving. Thus, it is safe to say that the business analytics market is here to grow. There is no better time to start the journey towards a successful career in business analytics. 

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