Causal Inference: Understanding Associations and Effects

This lecture by the instructor covers the topic of Causal Inference, focusing on understanding the difference between association and causation in data analysis. It delves into the concept of potential outcomes, counterfactual random variables, and the average causal effect. The lecture also discusses the limitations of association in inferring causation, using examples to illustrate the difference. Through the exploration of binary setups and causal odds ratios, the lecture provides insights into how to distinguish between association and causation in statistical analysis.