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DEEP CAUSAL LEARNING FOR CONTINUOUS TESTING, DIAGNOSIS, AND OPTIMIZATION
DEEP CAUSAL LEARNING FOR CONTINUOUS TESTING, DIAGNOSIS, AND OPTIMIZATION
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机译:进行深入的因果学习,以进行连续的测试,诊断和优化
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摘要
A system and methods for multivariant learning and optimization repeatedly generate self-organized experimental units (SOEUs) based on the one or more assumptions for a randomized multivariate comparison of process decisions to be provided to users of a system. The SOEUs are injected into the system to generate quantified inferences about the process decisions. Responsive to injecting the SOEUs, at least one confidence interval is identified within the quantified inferences, and the SOEUs are iteratively modified based on the at least one confidence interval to identify at least one causal interaction of the process decisions within the system. The causal interaction can be used for testing, diagnosis, and optimization of the system performance.
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