| 1 - Introduction.mp4 | 3.3 MB | ||
| 1 - Welcome.mp4 | 14 MB | ||
| 10 - Recap.mp4 | 5.6 MB | ||
| 2 - Causal Quantities of Interest.mp4 | 118.8 MB | ||
| 2 - Challenges with Causal AI.mp4 | 31.2 MB | ||
| 2 - Domain Expertise.mp4 | 15.4 MB | ||
| 2 - Estimand & Conditional Ignorability.mp4 | 168.5 MB | ||
| 2 - Layer 1 Explained.mp4 | 13 MB | ||
| 2 - What are Causal DAGs.mp4 | 47.1 MB | ||
| 2 - What is Causal AI.mp4 | 16.1 MB | ||
| 3 - Causal Discovery Algorithms Categories.mp4 | 10.9 MB | ||
| 3 - Considerations, Recommendations & Closure.mp4 | 35.7 MB | ||
| 3 - Do-operator in light of Causal DAGs.mp4 | 58.3 MB | ||
| 3 - Layer 1 Techniques.mp4 | 15.4 MB | ||
| 3 - Probabilities as the foundation of Causal Quantities.mp4 | 10.7 MB | ||
| 3 - S-Learner.mp4 | 45.3 MB | ||
| 3 - Simpson's Paradox.mp4 | 91.4 MB | ||
| 4 - Backdoor Adjustment.mp4 | 53.1 MB | ||
| 4 - Causal Discovery Algorithms Assumptions.mp4 | 27.1 MB | ||
| 4 - Graph Independence & Information Flows.mp4 | 27.6 MB | ||
| 4 - Layer 2 Explained.mp4 | 70.1 MB | ||
| 4 - T-Learner.mp4 | 38.8 MB | ||
| 4 - The Need for Causality in Business.mp4 | 41.8 MB | ||
| 5 - Causation and its relation to Association.mp4 | 112 MB | ||
| 5 - Constraint-based Causal Discovery.mp4 | 123.3 MB | ||
| 5 - Frontdoor Adjustment.mp4 | 41.2 MB | ||
| 5 - Graph Patterns.mp4 | 34 MB | ||
| 5 - Layer 2 Techniques.mp4 | 7.7 MB | ||
| 5 - X-Learner.mp4 | 125.8 MB | ||
| 6 - Blocking Paths & D-separation.mp4 | 40.7 MB | ||
| 6 - Do-calculus.mp4 | 190.3 MB | ||
| 6 - Layer 3 Explained.mp4 | 36.2 MB | ||
| 6 - Matching.mp4 | 28.3 MB | ||
| 6 - RCT's The Golden Standard for Causal Inference.mp4 | 125.9 MB | ||
| 6 - Score-based Causal Discovery.mp4 | 49.7 MB | ||
| 7 - Course Outline.mp4 | 12.4 MB | ||
| 7 - From Graph (In)dependence to Statistical (In)dependence.mp4 | 29.8 MB | ||
| 7 - Function-based Causal Discovery.mp4 | 40.5 MB | ||
| 7 - Inverse Probability Weighting.mp4 | 70.4 MB | ||
| 7 - Layer 3 Techniques.mp4 | 69.6 MB | ||
| 7 - PositivityUnconfoundedness Trade-Off.mp4 | 39.3 MB | ||
| 8 - Continuous Optimization-based Causal Discovery.mp4 | 18.6 MB | ||
| 8 - Do-operator in light of Structural Causal Models.mp4 | 11 MB | ||
| 8 - Recap.mp4 | 20.1 MB | ||
| 8 - Systematic vs. Random Errors.mp4 | 28.6 MB | ||
| 9 - Causal Discovery in Practice Hybrid & Iterative.mp4 | 12.5 MB | ||
| 9 - Recap.mp4 | 6.1 MB | ||
| Bonus Resources.txt | 409.6 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 56 total files | |||
Causal AI: An Introduction
https://FreeCourseWeb.com
Published 8/2024
Created by CausAI B.V.
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 55 Lectures ( 6h 45m ) | Size: 2.26 GB
Learn the foundational components of Causal Artificial Intelligence
What you'll learn:
What Causality is
The relationship between Causation and Association
Why RCT's are the golden standard for Causal Inference
Main components of Pearlian Framework for Causality: Ladder of Causation, Causal Graphs, Do-calculus, Structural Causal Models
Machine Learning & Propensity Score-based Causal Effect Estimators
Causal Discovery (Algorithms)
How to estimate Average Causal Effects using observational data (covering the entire end-to-end process)
Requirements:
Basic Probability and Statistics knowledge
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