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【今月発売の電子書籍】「人物叢書」新刊1点について

書誌事項末尾の【 】内は当館請求記号です。1. 参考文献近藤安太郎 著『系図研究の基礎知識 : 家系にみる日本の歴史 第4巻 (総括)』（近藤出版社 1990 【GB43-E61】）[備考] 系図書の歴史や系図調査の方法について解説しています。丸山浩一 編『系図文献資料総覧』（増補改訂 緑蔭書房 1992 【GB1-E34】）[備考] 基本史料や文献の紹介、都道府県別・所蔵機関別の系図文献の目録、家名ごとの参考文献を収録しています。太田亮 著, 丹羽基二 編『家系系図の入門』（増補 東洋書院 1995 【GB43-H227】）[備考] 「親族の調査」、「苗字の調査」、「系譜の沿革とその種類」、「偽系図の研究」などの項目があります。太田亮 著『家系系図の合理的研究法』（立命館大学出版部 1930 【610-8】）がもとになっています。丹羽基二 著『姓氏・家系・家紋の調べ ...

タイトル: Scalable individual treatment effect estimator for large graphs著者: Lin, Xiaofeng; Han, Bao,; Cui, Yan; Takeuchi, Koh; Kashima, Hisashi抄録: Causal inference plays a critical role in decision-making processes about whether to provide treatment to individuals across various domains, such as education, medicine, and e-commerce. One of the fundamental tasks in causal inference is to estimate the individual treatment effect (ITE), which represents the effect of a treatment on an individual outcome. Recently, many studies have focused on estimating ITE from graph data taking into account not only the covariates of units but also connections among them. In such a case, the outcome of a unit can be affected by not only its own covariates and treatment but also those of its neighbors, which is referred to as interference. Existing methods have utilized graph neural networks (GNNs) to capture interference and achieved improvements in estimating ITE on graph data. However, the ...

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