摘要 : Introduction. The need to establish causality covers a fairly wide range of different industries with different specifics and approaches. Therefore, it becomes necessary to apply various methods to solve the assigned tasks (in the... 展开 Introduction. The need to establish causality covers a fairly wide range of different industries with different specifics and approaches. Therefore, it becomes necessary to apply various methods to solve the assigned tasks (in the context of causality), which is accompanied by the choice of a wide range of tools, depending on the task at hand. Purpose. The purpose of this work is a brief overview and analysis of modern methods, algorithms and technologies for detecting causation and the range of tasks in which the use of the appropriate tools takes place. Methods. Starting from the gold standards of causal identification and to more accurate, but limited by the range of conditions, algorithms, the current state, advantages and disadvantages of the use of tools are described. Result. The analysis of the current state of existing methods, algorithms and technologies for establishing causality is carried out the prospects for further development and improvement of tools for causal detection are examined. Conclusions. At the moment there is a large list of known methods, algorithms and technologies, there is a number of problems in which there is a need for more accurate detection of causality. The paper shows that most of the tools for establishing causality give good results for acyclic structures, at the same time they can give false positive conclusions for cyclic structures. Well-known world scientific institutions and leading corporations of computer technology are fruitfully engaged in the development and implementation of more and more perfect tools for establishing causality in order to develop automated software projects close to human thinking. 收起