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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
Shuliang Wang +2 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 0001, Hao Wang 0008
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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 0001, Hao Wang 0008
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Introduction to the foundations of causal discovery [PDF]
International Journal of Data Science and Analytics, 2016 This article presents an overview of several known approaches to causal discovery. It is organized by relating the different fundamental assumptions that the methods depend on. The goal is to indicate that for a large variety of different settings the assumptions necessary and sufficient for causal discovery are now well understood.
Eberhardt Frederick +1 more
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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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DISCOVERY OF CAUSALITY POSSIBILITIES
International Journal of Pattern Recognition and Artificial Intelligence, 2004Determining causality has been a tantalizing goal throughout human history. Proper sacrifices to the gods were thought to bring rewards; failure to make suitable observations were thought to lead to disaster. Today, data mining holds the promise of extracting unsuspected information from very large databases.
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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. For generative causes this would be a context with a low base rate of
Barberia, I. +3 more
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Relational Blocking for Causal Discovery
Proceedings of the AAAI Conference on Artificial Intelligence, 2011Blocking is a technique commonly used in manual statistical analysis to account for confounding variables. However, blocking is not currently used in automated learning algorithms. These algorithms rely solely on statistical conditioning as an operator to identify conditional independence. In this work, we present relational blocking
Matthew J. Rattigan +2 more
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Incremental Causal Discovery and Visualization
Proceedings of the Workshop on Interactive Data Mining, 2019Discovering causal relations from limited amounts of data can be useful for many applications. However, all causal discovery algorithms need huge amounts of data to estimate the underlying causal graph. To alleviate this gap, this paper proposes a novel visualization tool which incrementally discovers causal relations as more data becomes available ...
Anders Holst, Sepideh Pashami, Juhee Bae
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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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A new causal discovery heuristic
Annals of Mathematics and Artificial Intelligence, 2018zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Steven D. Prestwich +2 more
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