14min chapter

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#121 Exploring Bayesian Structural Equation Modeling, with Nathaniel Forde

Learning Bayesian Statistics

CHAPTER

Modeling Reality: Causal Inference and Data Science

This chapter explores the integration of philosophy, mathematical logic, and data science in the context of probabilistic modeling. It emphasizes the importance of causal inference methods, understanding confounding variables, and constructing robust models to accurately reflect uncertainties in data. The discussion also highlights the necessity of effective communication and continuous learning in addressing complex societal challenges through data analysis.

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