Prediction focuses on what will happen, while explanation focuses on why something happens. In many cases, prediction is enough when we only need accurate results. For example, in policy analysis, a model may predict which areas will have high crime rates without explaining the causes. However, explanation is important when policymakers want to design interventions. Understanding the reasons behind crime helps in creating better policies.
A causal diagram shows relationships between variables. In my research, employee engagement affects performance. However, there are confounders such as leadership quality, organizational culture, and resources. These factors influence both engagement and performance. To distinguish causality from prediction, we need proper research design, such as controlling for confounders or using experiments.