For several years, we have been taught that correlation has nothing to do with causation. Still, several business leaders, media outlets, and elected officials use casual claims with some misleading correlations. As a result, such claims are unnecessarily amplified, unscrutinized, and mistakenly utilized for guiding end-user decisions.
The extensive research in psychology and the behavioral domain highlights a range of systematic mistakes people make while handling data. For example, we often tend to gather evidence to confirm some of our preconceived notions while ignoring the information that goes against our pre-set hypothesis. We also try to neglect several essential aspects of generating the data. It is important to pay more attention to the available data, even if some information is missing.
It can often lead to mistakes and even avoidable disasters for companies, governments, and individuals. Undoubtedly, the world is immensely loaded with data, and it is common to get bombarded with an extensive range of facts and figures. Therefore, learning techniques to assess causal claims and data analysis at all levels is essential. One of the best ways to do this is by promoting an experimentation culture in the organizations.
How unsupported are casual claims harmful to organizations?
In 2013, eBay spent almost 50 million dollars on advertising over various search engines. An analysis and consultants revealed that areas with high advertisement visibility led to more sales. However, two economists later challenged these claims by stating that randomized controlled trials and natural experiments showed that these ads were a total waste of money because they targeted the same people who were otherwise also interested in shopping on eBay. The pre-existing purchase histories were the main reasons why advertisements to those people, but the eBay marketing team failed to appreciate this factor. Such scenarios with other advertising campaigns also provided valuable insights into the impact of establishing an improper connection between causality and correlation.
The empirical economics landscape has changed dramatically over the past few years, and the experts have developed new skills to assess casual relationships. The experts have also developed a casual inference toolkit to help leaders handle claims more carefully. This is just the beginning of a new perspective on data handling. In this scenario, leaders need not assume that correlation reflects causation; instead, they must analyze various relevant factors before establishing this connection. If you feel that correlation may not be causal, it is better to start with practical experimentation. It may help you with the decision-making process, and you will come out with more feasible outcomes. This new approach will be highly beneficial for organizations to come out with sound conclusions related to the massive amount of data circulated online. Experts at Global Investment Strategies suggest using tech-inspired tools and techniques to assess field data for better analysis.