This unit covers the practical components of building, validating, and deploying causal AI systems with an emphasis on explainability, bias detection, and data management. Students will learn the causal AI pipeline, available tools, the role of synthetic data, and how to move from strategy to implementation in enterprise (B2C) contexts while integrating causal and traditional AI methods.
Learning Objectives
- Analyze the stages of a causal AI pipeline and map current tools and software to appropriate pipeline tasks (data generation, causal discovery, estimation, validation, and deployment).
- Evaluate causal models and datasets for explainability and sources of bias, and propose measurable mitigation strategies including the use of synthetic data and fairness checks.
- Apply integration techniques to combine causal AI approaches with traditional machine learning workflows and produce an implementation plan for a B2C enterprise marketing scenario.
- Demonstrate data governance and responsible AI practices by documenting data management requirements, monitoring strategies, and ethical controls required for safe deployment of causal AI solutions.
Curriculum
- 1 Section
- 17 Lessons
- 30 hours
Expand all sectionsCollapse all sections
- Unit 3 — Implementing Responsible Causal AI17
- 1.1Summary20 mins
- 1.2Explainability20 mins
- 1.3Detecting Bias and Ensuring Responsible AI20 mins
- 1.4Summary20 mins
- 1.5Note20 mins
- 1.6The Causal AI Pipeline20 mins
- 1.7The Importance of Synthetic Data20 mins
- 1.8Current State of Tools and Software in Causal AI20 mins
- 1.9Summary20 mins
- 1.10Enterprise Marketing in a Business-to-Consumer Scenario20 mins
- 1.11Moving from Strategy to Building and Implementing Causal AI Solutions20 mins
- 1.12Summary20 mins
- 1.13Where We Stand Today20 mins
- 1.14Foundations of Causal AI20 mins
- 1.15The Causal AI Journey20 mins
- 1.16Integrating Causal AI and Traditional AI20 mins
- 1.17The Imperative for Managing Data20 mins
Instructor
Free
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