因果人工智能——简介
- 1 - Causality, Association & RCT's
- 1 - Welcome
- 2 - What is Causal AI
- 3 - Simpson's Paradox
- 4 - The Need for Causality in Business
- 5 - Causation and its relation to Association
- 6 - RCT's The Golden Standard for Causal Inference
- 7 - Course Outline
- 2 - The Ladder of Causation
- 1 - Introduction
- 2 - Layer 1 Explained
- 3 - Layer 1 Techniques
- 4 - Layer 2 Explained
- 5 - Layer 2 Techniques
- 6 - Layer 3 Explained
- 7 - Layer 3 Techniques
- 8 - Do-operator in light of Structural Causal Models
- 9 - Recap
- 3 - Causal Directed Acyclic Graphs
- 1 - Introduction
- 2 - What are Causal DAGs
- 3 - Do-operator in light of Causal DAGs
- 4 - Graph Independence & Information Flows
- 5 - Graph Patterns
- 6 - Blocking Paths & D-separation
- 7 - From Graph (In)dependence to Statistical (In)dependence
- 8 - Recap
- 4 - Causal Inference Part 1 Identification
- 1 - Introduction
- 2 - Estimand & Conditional Ignorability
- 3 - Probabilities as the foundation of Causal Quantities
- 4 - Backdoor Adjustment
- 5 - Frontdoor Adjustment
- 6 - Do-calculus
- 7 - PositivityUnconfoundedness Trade-Off
- 8 - Recap
- 5 - Causal Inference Part 2 Estimation
- 1 - Introduction
- 2 - Causal Quantities of Interest
- 3 - S-Learner
- 4 - T-Learner
- 5 - X-Learner
- 6 - Matching
- 7 - Inverse Probability Weighting
- 8 - Systematic vs. Random Errors
- 9 - Recap
- 6 - Causal Discovery
- 1 - Introduction
- 2 - Domain Expertise
- 3 - Causal Discovery Algorithms Categories
- 4 - Causal Discovery Algorithms Assumptions
- 5 - Constraint-based Causal Discovery
- 6 - Score-based Causal Discovery
- 7 - Function-based Causal Discovery
- 8 - Continuous Optimization-based Causal Discovery
- 9 - Causal Discovery in Practice Hybrid & Iterative
- 10 - Recap
- 7 - Closure
- 1 - Introduction
- 2 - Challenges with Causal AI
- 3 - Considerations, Recommendations & Closure