At the recent SAP Innovation Day for IT, part of the SAP Business Campus event, Sebastián García, AI & Data Solutions Expert at Pyxis, delivered an insightful presentation on the limitations of traditional statistical methods in business analytics. He emphasized the importance of causal inference in uncovering cause-and-effect relationships between variables, enabling businesses to move beyond mere correlation and directly influence outcomes. García highlighted how this approach can optimize key areas such as supply chain management, customer retention, and manufacturing efficiency.  

Beyond Correlation: The Need for Causal Inference 

Leveraging advanced analytics to drive decision-making is essential for maintaining a competitive edge in modern business. Yet, while traditional statistical methods have long been used to identify patterns and predict outcomes, they often fall short of explaining the “why” behind those outcomes. With causal inference, models go beyond correlation to uncover the cause-and-effect relationships between variables.  

 A key limitation of traditional statistical tools is that they are excellent at detecting correlations but often fail to explain causality. “This is where causal inference makes a difference,” said García. “By asking deeper questions—such as ‘What will happen if this scenario did not happen?’—causal inference allows businesses to go beyond what is likely to happen, to retrospect and imagine, and focus on how they can influence outcomes.”  

Applications of Causal Inference in Business  

Causal inference can be applied across multiple areas of business, driving more effective strategies in key areas such as supply chain optimization, customer retention, and manufacturing process efficiency. “Businesses that adopt causal inference are better positioned to make data-driven decisions that go beyond correlation, unlocking more value from their data,” he said. It also reduces costs, as they are able to refine models and strategies before implementing them.  

In contrast to traditional machine learning methods, which answer questions like “What products are likely to be bought based on past behavior?”, causal inference helps answer, “What specific action will increase the likelihood of a purchase?” This shift from prediction to intervention can have significant implications for revenue growth, customer engagement, and operational efficiency.  

Identifying Hidden Variables 

Without implementing causal inference, “Sometimes, when one variable impacts another, there is a hidden variable in play, and if it is not accounted for, it can negatively affect business operations,” said García. By asking more specific questions, businesses are able to understand where their weaknesses lay, and enhance them with immediate effect.  

A structured, step-by-step process when applying causal inference involves defining the causal question, mapping the relationships between variables, determining the cause-and-effect relationships, estimating its effect, and validating and refining the model. To define the causal question, there must be a deep understanding of the business activities and the impact they have on operations (acquire quality data), to then identify the desired effect and estimate a better-than-before result, according to García.  

Unlocking Smarter Decision-Making 

By integrating causal inference into their AI-driven solutions, companies across industries can answer the how and why of business outcomes—leading to smarter, better forecasting and more impactful decisions. “With counterfactual scenarios, we can see what would have happened if no action had been taken, leading to better sales and customer retention and engagement,” said García. 

If you’re interested in delving deeper into the application of causal inference in business analytics, feel free to connect with us. Let’s explore how these insights can drive success in your organization.