Research On Causality Of Social Science Based On Big Data

Causality,as an ancient philosophical question,was originally richly interpreted in history.Specific to the social sciences,the exploration of causality faces difficulties that are different from those of natural science.The research methods used in causal inference in social science research are complicated and make researchers incomprehensible.The popular concept of big data era,which attaches importance to relevance,has made the causality problem the focus.The argument of "doing no relevant cause and effect" has been supported in many practical fields.The "fourth paradigm" concept proposed by big data in many research fields in recent years also requires an explanation of the metaphysical basis behind the big data approach.In the context of big data and the world,how to interpret causality is both a theoretical problem and a practical problem that need to be solved.On the basis of combing the relationship between the existing metaphysical causal theory and the corresponding causal inference method,this paper examines the substantive transformation of the existing method of causality in the social sciences by the method of big data research,and attempts to explore the metaphysical basis behind the changes of this research method.The evolution of the metaphysical approach to the big data research method.The paper uses a combination of comparative analysis and argumentation,text interpretation and case studies.Focusing on the diachronic evolution of causality from three basic perspectives of empiricism,manipulability theories of causation,and causal mechanism theory in the field of social science philosophy.First of all,it analyzes the traditional approach of empiricism and the corresponding social scientific research methods and its interpretation dilemma,and explains the inheritance and development of the empirical and causal concept of the new empirical approach of big data,and believes that data has its own life.Secondly,based on the manipulability theories of causation,this paper discusses the transformation of the big data related technology to the operationalist social science research method,and believes that this transformation reflects the extension of cognitive thesis,but the opacity brought about by the extension of knowledge,society science should accept the epistemology of non-anthropocentrism.In the end,it is believed that the mechanism of causality theory can really move from relevance to causality.Combined with the case study of using big data to study the history of ideas,it illustrates the difficulty in solving the process tracking method by big data research methods.Furthermore,the causal mechanism is reflected,and the causal mechanism should be positioned between understanding and interpretation.Big data provides the "life form" and "context" of the causal mechanism.