On causal representativeness of training samples of precedents in diagnostic type tasks

The work focuses on some features of causality analysis in data mining tasks. The possibilities of using so-called open logic theories in diagnostic (classification) tasks to describe replenished sets of empirical data are discussed. In tasks of this type, it is necessary to establish (predict, diagnose, etc.) the presence or absence of a target property in a new precedent given by a description in the same presentation language of heterogeneous data, which describes examples having a target property and counter-examples not having a target property. The variant of construction of open theories describing collections of precedents by means of special logical expressions — characteristic functions — is presented. Characteristic functions allow to get rid of heterogeneity in descriptions of precedents. The procedural design of formation of characteristic functions of a training sample of precedents is proposed. The properties of characteristic functions and some conditions of their existence are studied.