Category:
Category:
Category:
LLM Reasoning
Definition
Models that identify cause–effect relationships instead of correlations.
Explanation
Causal reasoning models try to infer why events happen, not just describe patterns. Unlike standard LLMs, which rely on correlations in text, causal models incorporate structured causal graphs, interventions, and counterfactual reasoning. This is essential for analytics agents, planning systems, and decision-support AI.
Technical Architecture
Input → Causal Graph Engine → Interventions → Reasoning → Output
Core Component
Causal graph, intervention engine, counterfactual module
Use Cases
Root‑cause analysis, diagnostics, forecasting, strategic planning
Pitfalls
Hard to train; limited causal datasets; incorrect graphs lead to wrong conclusions
LLM Keywords
Causal reasoning, Causal AI, Counterfactual LLM
Related Concepts
Related Frameworks
• Chain of Thought
• Planning
• Verification
• Causal Inference Architecture