Study of Causal Analysis of Categorical Variables based Information Theory

在这项研究中，因果分析的分类变量使用logit，loglinear，广义线性模型进行了研究。通径分析的常用方法是基于线性回归模型，LISREL模型广泛应用于心理学、经济学、社会科学等诸多科学领域。另一方面，在分类变量的通径分析中，不能假设变量之间存在线性关系，如何评估因果效应是一个难题。第一年研究了基于logit模型的通径分析。因果效应，即直接和间接效应，通过基线对数比值比定义，分配给每个路径的路径系数由Kullaback-Leibler信息定义，该信息描述了所采用的模型与解释变量和响应变量独立的模型之间的距离。结果报告的文件：路径分析的分类变量的基础上logit模型，上述文件提交出版。在最后一年，路径分析的对数线性和广义线性模型的基础上，被认为是在本研究的第一年发展的结果。解释变量的因果效应在实证研究和观察研究中进行了讨论。本文的研究成果是：广义线性模型的效应分析。

In this study, causal analysis of categorical variables by using logit, loglinear, and generalized linear models was studied. The usual method for path analysis is based on linear regression models, and LISREL model is widely applied to many scientific fields, e.g. psychology, economics, social science, etc. On the other hand, in path analysis of categorical variables linear relationships among the variables cannot be assumed, and it is a difficulty question how the causal effects are assessed. In the first year, path analyisis based on logit models was studied. The causal effects, i.e. direct and indirect effects, were defined through baseline log odds ratio, and the path coefficient to be assigned to each path was defined by Kullaback-Leibler information that describes the distance between an employed model and the model that explanatory variables and response variables are independent. The results are reported in the paper :Path analysis of categorical variables based on logit models,and the above paper is submitted for publication. In the final year, path analysis based on loglinear and generalized linear models was considered by developing the results in the first year of this research. Causal effects of explanatory variables were discussed in both empirical and observational studies. I am getting the research completed as the paper :Effect analysis in generalized linear models.