Summary

This chapter introduced the concept of causality and its importance across various fields. A brief historical overview acknowledged the contributions of ancient philosophers and modern statisticians in developing causal inference methods. We started by defining causal inference and distinguishing it from association and correlation with practical examples. We also touched on complex ideas such as potential outcomes, confounding variables, and Simpson’s paradox, explaining how they affect causal studies.

Finally, the chapter underscored the importance of causal inference in making informed decisions in our data-driven world. This foundation prepares you for a deeper exploration of causal inference in subsequent chapters.