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Explore categorical variables and causal inference in this statistical modeling lecture that demonstrates how to handle different types of categorical data and understand their role in establishing causal relationships. Learn to work with nominal and ordinal categories, implement proper coding schemes for categorical predictors, and distinguish between correlation and causation in statistical models. Discover techniques for modeling categorical outcomes, understand the challenges of causal identification, and practice interpreting results when categories serve as both predictors and outcomes in regression frameworks. Master the conceptual foundations needed to avoid common pitfalls when working with categorical data in causal analysis, including issues of confounding and selection bias that frequently arise in observational studies.
