Identification of cause-and-effect relationships when covering causes

The tasks of identification of cause-and-effect relationships are of great importance in medical diagnostics, finding the root causes of failures in software and hardware systems, and information security. The explainability of the formed conclusions obtained as a result of complex calculations using artificial intelligence methods is most often realized using causal relationships. The paper investigated the possibility of identification of cause-and-effect relationships in cases where the cause is in an inseparable object available for observation. In such cases, it is said that the "cause" property is covered by an object in which other data properties are present. Effects of causes appear in other information spaces. The cause-and-effect identification problem is investigated in the presence of other random data not related to the relationship generated by the cause-and-effect relationship. The model of deterministic cause-and-effect relationship is considered in the presence of a significant number of randomly occurring properties that are not related to the causal effect of some properties on others.