10. Causality & Domain Adaptation
Causality and Domain Adaptation are pivotal in machine learning by enabling models to comprehend underlying mechanisms beyond mere data patterns. Causality equips models to reason about 'why' events occur, ensuring fairness and robustness, while Domain Adaptation addresses real-world shifts by transferring knowledge across different contexts. Together, they enhance the reliability and interpretability of AI systems.
Sections
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What we have learnt
- Causality distinguishes between correlation and causation.
- Domain adaptation is necessary for models to generalize across different data distributions.
- Causal mechanisms are generally stable across domains while non-causal associations can vary.
Key Concepts
- -- Causality
- The relationship that indicates one event or variable affects another, distinguishing it from mere correlation.
- -- Causal Graphs
- Directed Acyclic Graphs (DAGs) that represent causal relationships among variables through nodes and edges.
- -- Domain Adaptation
- The process of adapting a machine learning model trained on one domain to perform well on a different but related domain.
- -- Invariant Causal Prediction (ICP)
- A method of learning predictive models that maintain performance stability across various environments.
Additional Learning Materials
Supplementary resources to enhance your learning experience.