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Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Purpose Air pollution has been linked to several neurological conditions, including stroke and neurodegenerative diseases. Evidence regarding its association with multiple sclerosis (MS) remains conflicting, limited by small sample sizes. Methods PubMed, Embase, Scopus, and Cochrane controlled register of trials (CENTRAL) were searched on ...
Ahmad A. Toubasi, Thuraya N. Al‐Sayegh
wiley +1 more source
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Causal Discovery via Causal Star Graphs
ACM Transactions on Knowledge Discovery From Data, 2023Discovering causal relationships among observed variables is an important research focus in data mining. Existing causal discovery approaches are mainly based on constraint-based methods and functional causal models (FCMs). However, the constraint-based method cannot identify the Markov equivalence class and the functional causal models cannot identify
Boxiang Zhao, Shuliang Wang, Lianhua Chi
exaly +2 more sources
Causal KL: Evaluating Causal Discovery
2022The two most commonly used criteria for assessing causal model discovery with artificial data are edit-distance and Kullback-Leibler divergence, measured from the true model to the learned model. Both of these metrics maximally reward the true model. However, we argue that they are both insufficiently discriminating in judging the relative merits of ...
O'Donnell, R T, Korb, K B, Allison, L
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Causal Discovery Using A Bayesian Local Causal Discovery Algorithm
2004This study focused on the development and application of an efficient algorithm to induce causal relationships from observational data. The algorithm, called BLCD, is based on a causal Bayesian network framework. BLCD initially uses heuristic greedy search to derive the Markov Blanket (MB) of a node that serves as the “locality” for
Subramani, Mani, Gregory F, Cooper
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Choosing Optimal Causal Backgrounds for Causal Discovery
Quarterly Journal of Experimental Psychology, 2010In two experiments, we studied the strategies that people use to discover causal relationships. According to inferential approaches to causal discovery, if people attempt to discover the power of a cause, then they should naturally select the most informative and unambiguous context.
Barberia, I. +3 more
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9th IEEE International Conference on Cognitive Informatics (ICCI'10), 2010
The standard causal discovery assumes that all variables are available from the beginning. In this paper, we consider an untouched scenario in which not all variables are available in advance. We call this scenario online causal discovery which assumes that the target of interest is given in advance while the other variables are unknown.
Kui Yu, Xindong Wu, Hao Wang
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The standard causal discovery assumes that all variables are available from the beginning. In this paper, we consider an untouched scenario in which not all variables are available in advance. We call this scenario online causal discovery which assumes that the target of interest is given in advance while the other variables are unknown.
Kui Yu, Xindong Wu, Hao Wang
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Local Causal Discovery Without Causal Sufficiency
Proceedings of the AAAI Conference on Artificial IntelligenceLocal causal discovery is crucial for revealing the causal relationships between specific variables from data. Existing local causal discovery algorithms are designed under the assumption of causal sufficiency, which states that there are no latent common causes for two or more of the observed variables in data.
Zhaolong Ling +7 more
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Information-Theoretic Causal Discovery
2021It is well-known that correlation does not equal causation, but how can we infer causal relations from data? Causal discovery tries to answer precisely this question by rigorously analyzing under which assumptions it is feasible to infer causal networks from passively collected, so-called observational data. Particularly, causal discovery aims to infer
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Ensembling MML Causal Discovery
2004This paper presents an ensemble MML approach for the discovery of causal models. The component learners are formed based on the MML causal induction methods. Six different ensemble causal induction algorithms are proposed. Our experiential results reveal that (1) the ensemble MML causal induction approach has achieved an improved result compared with ...
Honghua Dai, Gang Li, Zhi-Hua Zhou
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