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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https://doi.org/10.1007/978-3-7091-9029-6 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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O. Subrt,M. Tichy,V. Vladyka,K. Hurt However, little research has been conducted for appropriate window sizes. We propose Detection Windows based on Hidden Markov Model (HMMDW) for time-varying causal discovery of time series. Firstly, a sliding window moves along the time series, an autoregressive model with an external variable and

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J. I. Ruz-Franzi,J. M. González-Dardernt 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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J. I. Ruz-Franzi,J. M. González-Darderso prior research has predominantly relied on synthetic datasets for validation. These synthetic or semi-real datasets, controlled artificially, may not fully reflect an algorithm’s performance in real-world scenarios. Therefore, we proposed a method for evaluating causal discovery in the absence of

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Conference proceedings 2025, 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..

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https://doi.org/10.1007/978-981-97-7812-6Artificial Intelligence for Science; Big Data Analysis; Causal Inference; Large Language Model; Machine

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