Statistical Causal Inference and Media Studies

Abstract

　Statistical causal inference refers to the effort to clarify causal directions,

which go beyond simple correlation. Causal inference using survey data is an

extremely useful analytical tool in social science, which is a comparatively difficult

subject for carrying out experimental research. Its techniques are becoming

more sophisticated and its applications are rapidly expanding. However, in

media studies, analysis of statistical survey data utilizing causal inference is

rarely carried out, especially in Japan.

　　In this article, in an effort to deepen the understanding of causal inference

for the readers, the author investigates what significance it holds with regard

to media studies. In addition, the author explains the basic concepts and the

analytical framework of causal inference using longitudinal panel survey data.

Panel survey data has many advantages in carrying out causal inference, but

there are also several weak points. In contrast, though cross-sectional survey

data is generally regarded as less suitable for causal inference, it has a practical

advantage to compensate for the weak points of panel surveys.