Causal Inference: Understanding Treatment Effects

This lecture on causal inference delves into understanding treatment effects through random assignments, counterfactual functions, and causal regression functions. It covers the definition of causal effects, conditional causal effects, observational studies, confounding variables, and adjusted treatment effects. The instructor emphasizes the importance of controlling for confounders and the challenges of interpreting results from observational studies. The lecture also explores Simpson's paradox in binary data and clarifies the distinction between mathematical interpretations and English translations in causal relationships.