Vector Search As Nearest Neighbor Matching: RAG-based Policy Learning in Causal Inference

We propose one-step and two-step methods for policy learning with retrieval-augmented generation (RAG).

We formulate RAG-based action selection under the potential outcome framework.

In the two-step method, vector search retrieves action-specific neighboring evidence in an embedding space, the generator estimates conditional expected outcomes or their contrasts, and a plug-in rule selects an action.

This formulation connects action-specific vector search with nearest-neighbor matching in causal inference.

We decompose the regret of the two-step method into candidate-generation regret and within-candidate choice regret, and we bound the latter using prediction-error guarantees for nearest-neighbor estimators and transformers.

We evaluate the one-step method directly as a policy because its intermediate computation is unobserved.