Graph Patterns
Graph Patterns
Causal AI: A Theoretical Introduction
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Causality, Association & RCT's
Welcome
Slides
How to Use This Course
Course Guidelines
What is Causal AI?
Simpson's Paradox
The Need for Causality in Business
Causation and its relation to Association
RCT's: The Golden Standard for Causal Inference
Course Outline
Quiz Section 1
The Ladder of Causation
Introduction
Layer 1 Explained
Layer 1 Techniques
Layer 2 Explained
Layer 2 Techniques
Layer 3 Explained
Layer 3 Techniques
Do-operator in light of Structural Causal Models
Recap
Quiz Section 2
Causal Directed Acyclic Graphs
Introduction
What are Causal DAGs?
Do-operator in light of Causal DAGs
Graph Independence & Information Flows
Graph Patterns
Blocking Paths & D-separation
From Graph (In)dependence to Statistical (In)dependence
Recap
Quiz Section 3
Causal Inference Part 1: Identification
Introduction
Estimand & Conditional Ignorability
Probabilities as the foundation of Causal Quantities
Backdoor Adjustment
Frontdoor Adjustment
Do-calculus
Positivity/Unconfoundedness Trade-Off
Recap
Quiz Section 4
Causal Inference Part 2: Estimation
Introduction
Causal Quantities of Interest
S-Learner
T-Learner
X-Learner
Matching
Inverse Probability Weighting
Systematic vs. Random Errors
Recap
Quiz Section 5
Causal Discovery
Introduction
Domain Expertise
Causal Discovery Algorithms: Categories
Causal Discovery Algorithms: Assumptions
Constraint-based Causal Discovery
Score-based Causal Discovery
Function-based Causal Discovery
Continuous Optimization-based Causal Discovery
Causal Discovery in Practice: Hybrid & Iterative
Recap
Quiz Section 6
Closure
Introduction
Challenges with Causal AI
Considerations, Recommendations & Closure
Feedback and Reviews
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Causal AI: A Theoretical Introduction
Buy now
Learn more
Causality, Association & RCT's
Welcome
Slides
How to Use This Course
Course Guidelines
What is Causal AI?
Simpson's Paradox
The Need for Causality in Business
Causation and its relation to Association
RCT's: The Golden Standard for Causal Inference
Course Outline
Quiz Section 1
The Ladder of Causation
Introduction
Layer 1 Explained
Layer 1 Techniques
Layer 2 Explained
Layer 2 Techniques
Layer 3 Explained
Layer 3 Techniques
Do-operator in light of Structural Causal Models
Recap
Quiz Section 2
Causal Directed Acyclic Graphs
Introduction
What are Causal DAGs?
Do-operator in light of Causal DAGs
Graph Independence & Information Flows
Graph Patterns
Blocking Paths & D-separation
From Graph (In)dependence to Statistical (In)dependence
Recap
Quiz Section 3
Causal Inference Part 1: Identification
Introduction
Estimand & Conditional Ignorability
Probabilities as the foundation of Causal Quantities
Backdoor Adjustment
Frontdoor Adjustment
Do-calculus
Positivity/Unconfoundedness Trade-Off
Recap
Quiz Section 4
Causal Inference Part 2: Estimation
Introduction
Causal Quantities of Interest
S-Learner
T-Learner
X-Learner
Matching
Inverse Probability Weighting
Systematic vs. Random Errors
Recap
Quiz Section 5
Causal Discovery
Introduction
Domain Expertise
Causal Discovery Algorithms: Categories
Causal Discovery Algorithms: Assumptions
Constraint-based Causal Discovery
Score-based Causal Discovery
Function-based Causal Discovery
Continuous Optimization-based Causal Discovery
Causal Discovery in Practice: Hybrid & Iterative
Recap
Quiz Section 6
Closure
Introduction
Challenges with Causal AI
Considerations, Recommendations & Closure
Feedback and Reviews