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Titlebook: Causal Inference; 6th Pacific Causal I Xiao-Hua Zhou,Jinzhu Jia Conference proceedings 2025 The Editor(s) (if applicable) and The Author(s)

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樓主
發(fā)表于 2025-3-21 18:10:35 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱Causal Inference
副標(biāo)題6th Pacific Causal I
編輯Xiao-Hua Zhou,Jinzhu Jia
視頻videohttp://file.papertrans.cn/243/242040/242040.mp4
叢書(shū)名稱Communications in Computer and Information Science
圖書(shū)封面Titlebook: Causal Inference; 6th Pacific Causal I Xiao-Hua Zhou,Jinzhu Jia Conference proceedings 2025 The Editor(s) (if applicable) and The Author(s)
描述.This book constitutes the revised selected papers of the 6th Pacific Causal Inference Conference, PCIC 2024, held in Shanghai, China, during July 5–6, 2024...The 8 papers included in these proceedings were carefully reviewed and selected from 15 submissions. They aim to promote research and developmental activities in the fields of Causal Inference and Artificial Intelligence..
出版日期Conference proceedings 2025
關(guān)鍵詞Artificial Intelligence for Science; Big Data Analysis; Causal Inference; Large Language Model; Machine
版次1
doihttps://doi.org/10.1007/978-981-97-7812-6
isbn_softcover978-981-97-7811-9
isbn_ebook978-981-97-7812-6Series ISSN 1865-0929 Series E-ISSN 1865-0937
issn_series 1865-0929
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
The information of publication is updating

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發(fā)表于 2025-3-21 21:20:57 | 只看該作者
,Causal Inference in?the?Multiverse of?Hazard,biases and the ambiguity in defining populations for comparison across different treatment groups. To address these issues, we introduce a novel definition of counterfactual hazard based on the framework of possible worlds. Rather than conditioning on prior survival status as a conditional probabili
板凳
發(fā)表于 2025-3-22 01:25:51 | 只看該作者
,A Continuous Structural Intervention Distance to?Compare Causal Graphs, metric that extends beyond graph-based measures like Structural Hamming Distance and Structural Intervention Distance by incorporating underlying data alongside graph structures. Our approach embeds intervention distributions for each node pair as conditional mean embeddings in reproducing kernel H
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Real-World Implications of a Methodological Dilemma: Endogenous Confounding in Causal Decompositionnt youth development would be reduced and how much would remain? To address this question, a causal decomposition analysis must credibly identify the causal impact of intervening on the malleable factors. However, major confounders such as parental SES are intermediate outcomes of systemic racism ex
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D. Jeanmonod,M. Sindou,F. Mauguièrehe impact of both observed and unobserved confounders. Despite the multitude of proposed methods aimed at addressing observed confounding bias, these approaches are contingent upon the untestable assumption of Unconfoundedness, which posits the absence of unobserved confounders. In this paper, we pr
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