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-...
| Authors | Masahiro Kato, Taka Kato |
|---|---|
| Primary category | econ.EM |
| Published | 20 July 2026 |
| Last updated at source | 20 July 2026 |
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