Causality in data science is about understanding cause-and-effect relationships in data. While data analysis often reveals patterns and correlations, causality goes a step further by determining whether one event directly influences another. This is important because many decisions rely on knowing whether a specific action will produce a desired outcome, rather than just identifying patterns… Continue reading Understanding Causality in Data Science