因果人工智能——简介

下一讲： 2 - What is Causal AI

视频列表

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