Prediction Combination Where Contextual Recommendations Disagree
Abstract
Ordinal combinations and contextual Gaussian processes should be assessed through action changes rather than average numerical fit alone. Evaluation keeps separate-model recommendations and independently checks consequential disagreements. Sparse contexts expose dependence on borrowing. Agreement after combination does not prove that competing assumptions were resolved or that the chosen action transports to every population. This focused synthesis develops an empirical comparison that preserves the terminal action, observation process, and total verification cost. Its proposed tests are prospective; no new simulation run, model-training result, or intervention effect is reported.
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