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  1 - Causality, Association & RCT's

   2 - What is Causal AI.mp4

   4 - The Need for Causality in Business.mp4

   6 - RCT's The Golden Standard for Causal Inference.mp4

  2 - The Ladder of Causation

   2 - Layer 1 Explained.mp4

   4 - Layer 2 Explained.mp4

   6 - Layer 3 Explained.mp4

   8 - Do-operator in light of Structural Causal Models.mp4

  3 - Causal Directed Acyclic Graphs

   2 - What are Causal DAGs.mp4

   4 - Graph Independence & Information Flows.mp4

   6 - Blocking Paths & D-separation.mp4

   8 - Recap.mp4

  4 - Causal Inference Part 1 Identification

   2 - Estimand & Conditional Ignorability.mp4

   4 - Backdoor Adjustment.mp4

   6 - Do-calculus.mp4

   8 - Recap.mp4

  5 - Causal Inference Part 2 Estimation

   2 - Causal Quantities of Interest.mp4

   4 - T-Learner.mp4

   6 - Matching.mp4

   8 - Systematic vs. Random Errors.mp4

  6 - Causal Discovery

   10 - Recap.mp4

   3 - Causal Discovery Algorithms Categories.mp4

   5 - Constraint-based Causal Discovery.mp4

   7 - Function-based Causal Discovery.mp4

   9 - Causal Discovery in Practice Hybrid & Iterative.mp4

  7 - Closure

   2 - Challenges with Causal AI.mp4

  Bonus Resources.txt