Causal Inference & Directed Graphs

This lecture by the instructor covers the fundamental concepts of causal inference and directed graphs, focusing on conditional independence, directed acyclic graphs (DAGs), and fairness in algorithms. The lecture explains the relationships between variables, the Markov condition, d-Separation rules, and the implications of DAGs on independence relations. It also delves into the importance of fairness, transparency, and accountability in automated decision-making systems, discussing predictive parity, error rate balance, and statistical parity. Through examples and theorems, the lecture provides insights into modeling causal relationships and ensuring fairness in algorithmic decision-making.