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A Study on Causal Discovery Considering Confounders
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| Title: | A Study on Causal Discovery Considering Confounders |
| Other Titles: | 交絡因子を考慮した因果発見に関する研究 |
| Authors: | Song, Jing1 Browse this author |
| Authors(alt): | 宋, 静1 |
| Issue Date: | 25-Mar-2019 |
| Publisher: | Hokkaido University |
| Abstract: | There are a lot of observational data in the real world in which many variables are correlated with each other. Correlation is not equal to causality. The best way to demonstrate a causal relationship between variables is to conduct a controlled ran- domized experiment. However, real-world experiments are often expensive, unethical, or even impossible. Many researchers working in various fields are thus using statis- tical methods to analyze causal relationships between variables. Many studies have been conducted to infer causality from raw observational data, but most of them have been based on the assumption that all the variables (including confounders) affecting the causal relationships have been known. Today, however, emphasis has been placed on open data. In the open data environment, it is difficult to consider all related data beforehand, and an exploratory analysis is required to acquire data that can be confounding. Therefore, in this study, first, we analyzed how the existing methods which determines the causal direction between variables are influenced by unknown confounders. Through assessing the existing methods, we found that the existing methods are susceptible to confounding in different degrees. We thus investigated how to decide whether a third variable is confounding for two observed variables. Finally, we studied on a framework to perform causal analysis while considering the possible confounders. We have three purposes for the study. Firstly, investigating a general assessment method for causal discovery methods, especially investigating their performance when the data is confounded. Secondly, investigating how to de- termine a possible common cause variable. Thirdly, investigating how to do causal analysis of open data while considering the possible confounders. |
| Conffering University: | 北海道大学 |
| Degree Report Number: | 甲第13511号 |
| Degree Level: | 博士 |
| Degree Discipline: | 情報科学 |
| Examination Committee Members: | (主査) 准教授 小山 聡, 教授 栗原 正仁, 教授 山本 雅人, 教授 川村 秀憲, 教授 小野 哲雄 |
| Degree Affiliation: | 情報科学研究科(情報理工学専攻) |
| Type: | theses (doctoral) |
| URI: | http://hdl.handle.net/2115/74109 |
| Appears in Collections: | 課程博士 (Doctorate by way of Advanced Course) > 情報科学院(Graduate School of Information Science and Technology)
学位論文 (Theses) > 博士 （情報科学）
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