Doubly Robust Policy Evaluation and Learning¶
Dudík, M., Langford, & Li, L. (2011). Doubly Robust Policy Evaluation and Learning. Proceedings of the 28th International Conference on Machine Learning, 1097-1104.
Cited by¶
1 citation across 1 artifact.
Each citation links to the sentence it supports in the citing article.
Mechanisms¶
- Historical Replay
- Its deep limit is that logged history only records the outcomes of the actions that were actually taken, so replay cannot truly observe how the world would have responded to the new policy's different choices — the missing-counterfactual problem at the heart of off-policy evaluation.
This sourceFrames off-policy evaluation as estimating a new policy from historical contexts, chosen actions, and only the rewards those actions revealed, leaving outcomes under different actions unobserved.
- Its deep limit is that logged history only records the outcomes of the actions that were actually taken, so replay cannot truly observe how the world would have responded to the new policy's different choices — the missing-counterfactual problem at the heart of off-policy evaluation.
Verification¶
Does it exist? Not checked yet. This entry carries no identifier to resolve. It was extracted from the citation as written in the article, normalized, and deduplicated against the rest of the registry.
Does it back the claim? Not recorded. The single citation of this work carries no recorded support check.
Was it audited? Yes. A second, independent pass read the citation against the article text and recorded a verdict.
Support is checked per citation rather than per work — the same source can be cited soundly in one article and wrongly in another. Per-citation recording began recently, so a citation with no recorded check is a gap in the record rather than evidence it went unchecked.
See how references were verified.
Registry ID ref:1775f6c2aa66 · see in the full table