observational causal inference

Observational causal inference is a method used to determine whether one factor directly influences another using data collected without controlled experiments. Instead of randomly assigning treatments, researchers analyze existing information—like medical records or surveys—to identify patterns that suggest cause-and-effect relationships. Because the data isn't from controlled experiments, they use statistical techniques to account for other factors that might affect the outcome, aiming to infer whether changes in one variable genuinely lead to changes in another. This approach helps answer important questions about causality in real-world settings where controlled studies aren't always possible.