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DETERMINING FEATURE CONTRIBUTIONS TO DATA METRICS UTILIZING A CAUSAL DEPENDENCY MODEL
DETERMINING FEATURE CONTRIBUTIONS TO DATA METRICS UTILIZING A CAUSAL DEPENDENCY MODEL
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机译：利用因果依赖模型确定数据指标的功能贡献
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摘要
The present disclosure relates to methods, systems, and non-transitory computer-readable media for determining causal contributions of dimension values to anomalous data based on causal effects of such dimension values on the occurrence of other dimension values from interventions performed in a causal graph. For example, the disclosed systems can identify an anomalous dimension value that reflects a threshold change in value between an anomalous time period and a reference time period. The disclosed systems can determine causal effects by traversing a causal network representing dependencies between different dimensions associated with the dimension values. Based on the causal effects, the disclosed systems can determine causal contributions of particular dimension values on the anomalous dimension value. Further, the disclosed systems can generate a causal-contribution ranking of the particular dimension values based on the determined causal contributions.
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