Too many leaders take an incomplete approach to understanding empirical patterns, leading to costly mistakes and misinterpretations. As we have discussed before, one extremely common mistake is interpreting a misleading correlation as causal. We’ve advised countless organizations on the topic. We’ve written research papers, managerial articles, and even a book dedicated to the power of experiments and causal inference tools — a toolkit that economists have adopted and adapted over the past few decades. Yet, while we are deep believers in the causal inference toolkit, we’ve also seen the reverse problem — leaders who overlook useful patterns because they are not causal. The truth is, there are also times when a correlation is not only sufficient, but is exactly what is needed. The mistake leaders make here is failing to understand the distinction between prediction and causation. Or, more specifically, the distinction between predicting an outcome and predicting how a decision will affect an outcome.