Evaluation is critical to advance decision making across domains, yet existing methodologies often struggle to balance theoretical rigor and practical scalability. In order to reduce the cost of experimental evaluation, we introduce a computational theory of evaluation for parameterisable subjects. We prove upper bounds of generalized evaluation error and generalized causal effect error of evaluation metric on subject. We also prove efficiency, and consistency to estimated causal effect of subject on metric by prediction. To optimize evaluation models, we propose a meta-learner to handle heterogeneous evaluation subjects space. Comparing with other computational approaches, our (conditional) evaluation model reduced 24.1%-99.0% evaluation errors across 12 scenes, including individual medicine, scientific simulation, business activities, and quantum trade. The evaluation time is reduced 3-7 order of magnitude comparing with experiments or simulations.

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