COUNTERFACTUAL AND RECOURSE METHOD FOR RECOMMENDING NETWORK CONFIGURATIONS TOWARDS FAVORABLE OUTCOME

A method for improving communication network performance comprises identifying a favorability status of individual predictions and/or decisions of a plurality of decisions of a machine-learning algorithm acting on the communication network. The favorability statuses are stored with corresponding values of network parameters used as features in the algorithm. A counterfactual algorithm is generated, e.g., by generating a tree-based classification algorithm, based on the stored favorability statuses and network parameter values, to derive rules for producing a favorable status based on one or more of the network parameters. A proposed recourse action comprising a change in at least one of the network parameters is identified, based on the rules, and a decision network, such as a Bayesian inference network, is generated for determining a confidence level estimating a reliability of achieving a favorable status by changing the network parameter(s). Whether to implement the proposed recourse action is determined, based on the confidence level.