Definition
In the context of AI Agents and RAG, causal inference refers to the methodology of identifying whether a specific retrieved context or agentic action directly produced a particular output, rather than merely correlating with it. It involves using interventions or counterfactuals to verify the logical necessity of a data point in the model's reasoning chain, ensuring the system isn't 'hallucinating' a connection between unrelated chunks.
Distinguishing 'The model mentioned this because it was in the text' (Causation) from 'The model mentioned this and it happened to be in the text' (Correlation).
"A Light Switch: Flipping the switch (the retrieved chunk) is the only reason the light (the answer) turns on, as opposed to the light turning on because of a sensor or a timer."
- Counterfactual Prompting(Component)
- Attribution Analysis(Prerequisite)
- Faithfulness Metrics(Evaluation Framework)
Conceptual Overview
In the context of AI Agents and RAG, causal inference refers to the methodology of identifying whether a specific retrieved context or agentic action directly produced a particular output, rather than merely correlating with it. It involves using interventions or counterfactuals to verify the logical necessity of a data point in the model's reasoning chain, ensuring the system isn't 'hallucinating' a connection between unrelated chunks.
Disambiguation
Distinguishing 'The model mentioned this because it was in the text' (Causation) from 'The model mentioned this and it happened to be in the text' (Correlation).
Visual Analog
A Light Switch: Flipping the switch (the retrieved chunk) is the only reason the light (the answer) turns on, as opposed to the light turning on because of a sensor or a timer.