Interdependent causal networks for root cause localization

A method for identifying most probable root causes, comprising:

• detecting a system failure;
• conducting topological cause learning by extracting causal relations from entity metrics data and system key performance indicator (KPI) data;
• propagating the system failure over a learned causal graph;
• generating a topological cause score representing how much a component can be the root cause;
• generating an individual cause score based on entity metrics using extreme value theory;
• detecting anomalous entities based on performance of individual components;
• aggregating the topological cause score and individual cause score to obtain a root cause ranking to discover the most probable root causes;
• and
• identifying a top K system entities associated with the most probable root causes.

A system for identifying most probable root causes, comprising:

• one or more processors;
• a display screen coupled to the one or more processors through a bus;
• memory coupled to the one or more processors through the bus, wherein the memory includes a topological causal discover tool configured to system key performance indicator (KPI), detect a system failure, conducting topological cause learning by extracting causal relations from entity metrics data and system key performance indicator (KPI) data, propagate the system failure over a learned causal graph, and generate a topological cause score representing how much a component can be the root cause;
• an individual causal discovery tool configured to receive entity metrics, generate an individual cause score based on the entity metrics using extreme value theory, and detect anomalous entities based on performance of individual components;
• and
• an integration tool configured to aggregate the topological causal score and the individual causal score to obtain a root cause ranking to discover the most probable root causes, identify a top K system entities associated with the most probable root causes, wherein identifying most probable root causes does not require any domain/prior knowledge as input for root cause localization, and display the most probable root causes to a user on the display screen.