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Intro
Professor Jennifer Hill discusses the significance of causality in data science, covering topics such as distinguishing between correlation and causation, methods for confidently inferring causality in research, favorite causal analysis tools, and a new GUI for making causal inferences. This chapter is beneficial for a wide audience curious about causality and especially relevant for data scientists working with causal models.