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Causal Reasoning(CR)
Establishes and follows explicit cause-and-effect relationships
In 30 seconds
- What
- Traces effects backward through cause layers, identifies root causes, and models counterfactuals to plan interventions.
- When to use
- Problems with multiple contributing factors, where surface symptoms hide underlying mechanisms you must fix.
- Watch out
- Causal chains are speculative; correlations you identify may not reflect true causation without domain validation.
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Causal Reasoning: Overview
Establishes and follows explicit cause-and-effect relationships
- Cause-and-effect chain construction
- Root cause identification
- Intervention planning
- Counterfactual thinking
- Temporal relationship modeling
- Mechanism understanding
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References
The papers, specifications, and repositories this pattern is based on.
- Causal Inference in Statistics: A Primer (Pearl et al., 2016)
- Causal Reasoning and Large Language Models: Opening a New Frontier for CausalityarXiv:2305.00050
- The Book of Why: The New Science of Cause and Effect (Pearl & Mackenzie, 2018)
- Microsoft DoWhy: Causal Inference Framework
- Causal AI: OpenAI Approaches to Causal Reasoning
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