How to think about the relationship between correlation and causation

The concept that "correlation is not causation" is often misunderstood, leading to confusion in interpreting data, such as the misleading claims of LinkedIn Premium's upsell or anecdotal "aha moments" in user engagement. The text explores this confusion by examining the essential role of causal analysis, whether through formal methods such as causal inference models or informal intuition, as demonstrated in historical achievements. It introduces a simplified formula, "Correlation ≈ Causation + Selection Bias," to help discern the difference between genuine causal relationships and mere correlations affected by selection bias. The text emphasizes that recognizing selection bias is crucial in avoiding errors, such as assuming that an event like taking a pill or experiencing an "aha moment" is the direct cause of an observed effect, rather than a result of underlying factors. Various causal inference techniques, including randomized controlled trials and difference-in-differences, aim to eliminate selection bias, allowing for more accurate causal conclusions. Ultimately, the text warns against the temptation to accept correlations at face value, advocating for a critical approach to data analysis to ensure meaningful and informed decision-making.