Causality - Interventions and Counterfactuals

Learn the fundamentals of causal inference through this comprehensive lecture series focusing on interventions and counterfactuals in causal reasoning. Explore how to distinguish between correlation and causation, understand the mathematical frameworks for modeling causal relationships, and master the concepts of interventional distributions and counterfactual reasoning. Discover the do-calculus and its applications for identifying causal effects from observational data, examine Pearl's causal hierarchy, and investigate how counterfactual queries can be answered using structural causal models. Delve into practical techniques for optimizing causal objective functions and learn to apply these methods to real-world scenarios where understanding cause-and-effect relationships is crucial. Gain proficiency in using graphical models to represent causal assumptions, understand the conditions under which causal effects can be identified from data, and develop skills in reasoning about hypothetical scenarios and alternative worlds through counterfactual analysis.