Inferential causal explanatory journalism. A science-based bridge between outcome and cause in journalism

Abstract

This paper proposes a new journalistic discipline, ‘Inferential Causal Explanatory Journalism’, which combines scientific insights and methods with journalistic practices and meets audiences’ need for knowledge about complex causal relationships affecting everyday life. The paper outlines the theoretical framework and methodology that allows journalists to infer relations between news events and their causes instead of the standard referential practice. Inferential causal explanatory journalism positions journalists as independent and credible producers of knowledge. It leverages the explosive growth in online access to scientific studies, as well as various other sources of digital data on current phenomena in recent years, and suggests that AI technologies can make it less time-consuming for journalists to arrive at causal explanations.