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Journal articles on the topic 'Causal relation'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 February 2022

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1

Effendi, Yutika Amelia, and Nania Nuzulita. "Process Discovery of Business Processes Using Temporal Causal Relation." Journal of Information Systems Engineering and Business Intelligence 5, no. 2 (October 24, 2019): 183. http://dx.doi.org/10.20473/jisebi.5.2.183-194.

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Background: Nowadays, enterprise computing manages business processes which has grown up rapidly. This situation triggers the production of a massive event log. One type of event log is double timestamp event log. The double timestamp has a start time and complete time of each activity executed in the business process. It also has a close relationship with temporal causal relation. The temporal causal relation is a pattern of event log that occurs from each activity performed in the process.Objective: In this paper, seven types of temporal causal relation between activities were presented as an extended version of relations used in the double timestamp event log. Since the event log was not always executed sequentially, therefore using temporal causal relation, the event log was divided into several small groups to determine the relations of activities and to mine the business process.Methods: In these experiments, the temporal causal relation based on time interval which were presented in Gantt chart also determined whether each case could be classified as sequential or parallel relations. Then to obtain the business process, each temporal causal relation was combined into one business process based on the timestamp of activity in the event log.Results: The experimental results, which were implemented in two real-life event logs, showed that using temporal causal relation and double timestamp event log could discover business process models.Conclusion: Considering the findings, this study concludes that business process models and their sequential and parallel AND, OR, XOR relations can be discovered by using temporal causal relation and double timestamp event log.Keywords:Business Process, Process Discovery, Process Mining, Temporal Causal Relation, Double Timestamp Event Log

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2

Barrière, Caroline. "Investigating the causal relation in informative texts." Terminology 7, no. 2 (December 31, 2001): 135–54. http://dx.doi.org/10.1075/term.7.2.02bar.

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Our work investigates the causal relation as it is expressed in informative texts. We view causal relations as important because of the dynamic dimension they bring to a domain model. Thorough study of a corpus leads us to distinguish two prominent classes of indicators of the causal relation: conjunctional phrases, and verbs. This paper identifies multiple knowledge-rich patterns within each class and studies their usage, frequency and noise. Results from this manual investigation informs a discussion on the feasibility of automatic extraction of the different forms of expression of the causal relation.

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3

Mittag, Daniel M. "On the Causal-Doxastic Theory of the Basing Relation." Canadian Journal of Philosophy 32, no. 4 (December 2002): 543–59. http://dx.doi.org/10.1080/00455091.2002.10716530.

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If one is to believe that p justifiably, then one must believe p for, or because of, one's evidence or reasons in support of p. The basing relation is exactly this relation that obtains between one's belief and one's reasons for believing. Keith Allen Korcz, in a recent article published in this Journal, has argued that two conditions are each sufficient and are jointly necessary to establish basing relations between beliefs and reasons. One condition is formulated to account for basing relations that can obtain in virtue of causal relations between one's belief and reasons, and the other condition is supposed to account for basing relations which can be established independently of the instantiation of any such causal relation.

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Stoica, O. C. "Spacetime Causal Structure and Dimension from Horismotic Relation." Journal of Gravity 2016 (May 25, 2016): 1–6. http://dx.doi.org/10.1155/2016/6151726.

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A reflexive relation on a set can be a starting point in defining the causal structure of a spacetime in General Relativity and other relativistic theories of gravity. If we identify this relation as the relation between lightlike separated events (the horismos relation), we can construct in a natural way the entire causal structure: causal and chronological relations, causal curves, and a topology. By imposing a simple additional condition, the structure gains a definite number of dimensions. This construction works with both continuous and discrete spacetimes. The dimensionality is obtained also in the discrete case, so this approach can be suited to prove the fundamental conjecture of causal sets. Other simple conditions lead to a differentiable manifold with a conformal structure (the metric up to a scaling factor) as in Lorentzian manifolds. This structure provides a simple and general reconstruction of the spacetime in relativistic theories of gravity, which normally requires topological structure, differential structure, and geometric structure (which decomposes in the conformal structure, giving the causal relations and the volume element). Motivations for such a reconstruction come from relativistic theories of gravity, where the conformal structure is important, from the problem of singularities, and from Quantum Gravity, where various discretization methods are pursued, particularly in the causal sets approach.

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5

den Ouden, Hanny. "De Verwerving van de Negatief Causale Relatie." Toegepaste Taalwetenschap in Artikelen 53 (January 1, 1995): 83–94. http://dx.doi.org/10.1075/ttwia.53.08oud.

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Sanders, Spooren and Noordman (1992) provide a classification of coherence relations that is based on four primitives. These primitives are claimed to have a psychological status, in that hearers and speakers use their knowledge of these primitives to infer the right coherence relation between two clauses. The order in which children acquire coherence relations provides a test base for the classification: the classification predicts that negative causal relations are the most complex and that children therefore acquire these relations later than any of the others. This hypothesis was investigated in an experiment with 8- and 11-year-old children. In one task the children had to infer the right relation, in another task the children had to produce the right relation. Negative causal relations were compared with negative additive and positive causal relations. The items were constructed with nonsense words to eliminate the factor of world knowledge. In several respects the negative causal relation turned out to be the most complex.

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Michiels, J. J., and Th van Joost. "Erythromelalgia and thrombocythemia: A causal relation." Journal of the American Academy of Dermatology 22, no. 1 (January 1990): 107–11. http://dx.doi.org/10.1016/s0190-9622(08)80005-9.

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7

Helm, Sven, and Poul Erik Petersen. "Causal relation between malocclusion and caries." Acta Odontologica Scandinavica 47, no. 4 (January 1989): 217–21. http://dx.doi.org/10.3109/00016358909007704.

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8

NEWMAN, ANDREW. "The Causal Relation and its Terms." Mind XCVII, no. 388 (1988): 529–50. http://dx.doi.org/10.1093/mind/xcvii.388.529.

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9

Dubois, Didier, and Henri Prade. "Fuzzy relation equations and causal reasoning." Fuzzy Sets and Systems 75, no. 2 (October 1995): 119–34. http://dx.doi.org/10.1016/0165-0114(95)00105-t.

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10

Smith, Sheldon R. "Causation and Its Relation to ‘Causal Laws’." British Journal for the Philosophy of Science 58, no. 4 (December 1, 2007): 659–88. http://dx.doi.org/10.1093/bjps/axm036.

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Helm, Sven, and Poul Erik Petersen. "Causal relation between malocclusion and periodontal health." Acta Odontologica Scandinavica 47, no. 4 (January 1989): 223–28. http://dx.doi.org/10.3109/00016358909007705.

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12

Buehner, Marc J., and Jon May. "Rethinking Temporal Contiguity and the Judgement of Causality: Effects of Prior Knowledge, Experience, and Reinforcement Procedure." Quarterly Journal of Experimental Psychology Section A 56, no. 5 (July 2003): 865–90. http://dx.doi.org/10.1080/02724980244000675.

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Time plays a pivotal role in causal inference. Nonetheless most contemporary theories of causal induction do not address the implications of temporal contiguity and delay, with the exception of associative learning theory. Shanks, Pearson, and Dickinson (1989) and several replications (Reed, 1992, 1999) have demonstrated that people fail to identify causal relations if cause and effect are separated by more than two seconds. In line with an associationist perspective, these findings have been interpreted to indicate that temporal lags universally impair causal induction. This interpretation clashes with the richness of everyday causal cognition where people apparently can reason about causal relations involving considerable delays. We look at the implications of cause-effect delays from a computational perspective and predict that delays should generally hinder reasoning performance, but that this hindrance should be alleviated if reasoners have knowledge of the delay. Two experiments demonstrated that (1) the impact of delay on causal judgement depends on participants’ expectations about the timeframe of the causal relation, and (2) the free-operant procedures used in previous studies are ill-suited to study the direct influences of delay on causal induction, because they confound delay with weaker evidence for the relation in question. Implications for contemporary causal learning theories are discussed.

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13

Minguzzi, E. "In a distinguishing spacetime the horismos relation generates the causal relation." Classical and Quantum Gravity 26, no. 16 (July 30, 2009): 165005. http://dx.doi.org/10.1088/0264-9381/26/16/165005.

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박주화 and 도승이. "The Effect of a Functional form of Causal relation on Accurate Causal Reasoning." Korean Journal of Cognitive and Biological Psychology 27, no. 2 (April 2015): 201–30. http://dx.doi.org/10.22172/cogbio.2015.27.2.007.

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15

Chang-Ohk Moon. "Causal Relation and Experience in Hume and Whitehead." Studies in Philosophy East-West ll, no. 65 (September 2012): 77–94. http://dx.doi.org/10.15841/kspew..65.201209.77.

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Manage, Neela, and Michael L. Marlow. "The Causal Relation between Federal Expenditures and Receipts." Southern Economic Journal 52, no. 3 (January 1986): 617. http://dx.doi.org/10.2307/1059261.

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17

Dyreborg, Johnny. "The causal relation between lead and lag indicators." Safety Science 47, no. 4 (April 2009): 474–75. http://dx.doi.org/10.1016/j.ssci.2008.07.015.

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18

Spellman, Barbara A., and Dieynaba G. Ndiaye. "On the relation between counterfactual and causal reasoning." Behavioral and Brain Sciences 30, no. 5-6 (December 2007): 466–67. http://dx.doi.org/10.1017/s0140525x07002725.

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AbstractWe critique the distinction Byrne makes between strong causes and enabling conditions, and its implications, on both theoretical and empirical grounds. First, we believe that the difference is psychological, not logical. Second, we disagree that there is a strict “dichotomy between the focus of counterfactual and causal thoughts.” Third, we disagree that it is easier for people to generate causes than counterfactuals.

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19

Barrière, Caroline. "Hierarchical refinement and representation of the causal relation." Terminology 8, no. 1 (June 21, 2002): 91–111. http://dx.doi.org/10.1075/term.8.1.05bar.

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This research looks at the complexity inherent in the causal relation and the implications for its representation in a Terminological Knowledge Base (TKB). Supported by a more general study of semantic relation hierarchies, a hierarchical refinement of the causal relation is proposed. It results from a manual search of a corpus which shows that it efficiently captures and formalizes variations expressed in text. The feasibility of determining such categorization during automatic extraction from corpora is also explored. Conceptual graphs are used as a representation formalism to which we have added certainty information to capture the degree of certainty surrounding the interaction between two terms involved in a causal relation.

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20

Nasim, Bina, Ahmed Sajjad, Zafar Khan, Zulfiqar Ali, Anis Sheikh, Tanvir Yadgir, Wajahat Khan, Omer Sakaf, and Ghulam Naroo. "Prevalence of ACS and Causal Relation of Hypomagnesaemia." British Journal of Medicine and Medical Research 12, no. 7 (January 10, 2016): 1–5. http://dx.doi.org/10.9734/bjmmr/2016/19850.

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21

Korcz, Keith Allen. "The Causal-Doxastic Theory of the Basing Relation." Canadian Journal of Philosophy 30, no. 4 (December 2000): 525–50. http://dx.doi.org/10.1080/00455091.2000.10717542.

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The epistemic basing relation is the relation which must hold between a person's belief and the adequate reasons for holding that belief if the belief is to be epistemically justified by those reasons. Although the basing relation is a fundamental component of any adequate theory of epistemic justification, it has received scant attention in the literature. In this paper, I propose a novel causal analysis of the basing relation, one which helps to characterize an intemalist element which, I shall argue, is required of any successful account of epistemic justification, and which confirms current trends away from coherentist and reliabilist theories of justification.

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22

Brewin, Chris R. "Depression and causal attributions: What is their relation?" Psychological Bulletin 98, no. 2 (1985): 297–309. http://dx.doi.org/10.1037/0033-2909.98.2.297.

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23

Yang, Xuefeng, and Kezhi Mao. "Multi level causal relation identification using extended features." Expert Systems with Applications 41, no. 16 (November 2014): 7171–81. http://dx.doi.org/10.1016/j.eswa.2014.05.044.

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24

Hong, Ji-sup. "Measurement of Public Values and Their Causal Relation." Review of Institution and Economics 15, no. 3 (August 31, 2021): 89–132. http://dx.doi.org/10.30885/rie.2021.15.3.089.

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25

Yu, Kui, Lin Liu, and Jiuyong Li. "A Unified View of Causal and Non-causal Feature Selection." ACM Transactions on Knowledge Discovery from Data 15, no. 4 (June 2021): 1–46. http://dx.doi.org/10.1145/3436891.

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In this article, we aim to develop a unified view of causal and non-causal feature selection methods. The unified view will fill in the gap in the research of the relation between the two types of methods. Based on the Bayesian network framework and information theory, we first show that causal and non-causal feature selection methods share the same objective. That is to find the Markov blanket of a class attribute, the theoretically optimal feature set for classification. We then examine the assumptions made by causal and non-causal feature selection methods when searching for the optimal feature set, and unify the assumptions by mapping them to the restrictions on the structure of the Bayesian network model of the studied problem. We further analyze in detail how the structural assumptions lead to the different levels of approximations employed by the methods in their search, which then result in the approximations in the feature sets found by the methods with respect to the optimal feature set. With the unified view, we can interpret the output of non-causal methods from a causal perspective and derive the error bounds of both types of methods. Finally, we present practical understanding of the relation between causal and non-causal methods using extensive experiments with synthetic data and various types of real-world data.

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Hong, Jooyeon, and Wonsun Gamaijb@gmail com Paek. "Fraud Firms and the Matching Principle: Evidence from Korea." Gadjah Mada International Journal of Business 16, no. 2 (June 28, 2014): 167. http://dx.doi.org/10.22146/gamaijb.5462.

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This paper examines whether the degree of matching for poor-performing fraud firms varies depending on the strength of the causal relation between expenses and revenues. A stronger causal relation exists between revenues and operating expenses than between revenues and total expenses that include non-operating expenses as well as operating expenses. Fraud firms have stronger incentives for managing earnings. Given that managing earnings is easier when using non-operating items than when using operating items, the degree of matching is (not) lower for fraud firms than for non-fraud firms at the strong (weak) level of the causal relation between revenues and expenses. Empirical results suggest that the degrees of matching are different between fraud and non-fraud firms only at the strong level of the causal relation between revenues and expenses. This result implies that the investigation of the matching model at a strong level of the causal relation between revenues and expenses is more effective than that at a weak level of the causal relation, with regard to examining the degree of matching for fraud firms. This study contributes to the literature by providing evidence on the importance of the level of the causal relation when examining the degree of matching.

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27

Kim Duck Ki. "A Study on the Causal Relationship of Occupational Diseases -Focusing on Proximate Causal relation-." Journal of hongik law review 20, no. 3 (September 2019): 199–238. http://dx.doi.org/10.16960/jhlr.20.3.201909.199.

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28

Samad, Abdus. "Causal Relation Between Economic Growth And Fdi: Evidence from South and East Asia." Indian Journal of Applied Research 4, no. 6 (October 1, 2011): 123–27. http://dx.doi.org/10.15373/2249555x/june2014/37.

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29

Hassanzadeh, Oktie, Debarun Bhattacharjya, Mark Feblowitz, Kavitha Srinivas, Michael Perrone, Shirin Sohrabi, and Michael Katz. "Causal Knowledge Extraction through Large-Scale Text Mining." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 09 (April 3, 2020): 13610–11. http://dx.doi.org/10.1609/aaai.v34i09.7092.

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In this demonstration, we present a system for mining causal knowledge from large corpuses of text documents, such as millions of news articles. Our system provides a collection of APIs for causal analysis and retrieval. These APIs enable searching for the effects of a given cause and the causes of a given effect, as well as the analysis of existence of causal relation given a pair of phrases. The analysis includes a score that indicates the likelihood of the existence of a causal relation. It also provides evidence from an input corpus supporting the existence of a causal relation between input phrases. Our system uses generic unsupervised and weakly supervised methods of causal relation extraction that do not impose semantic constraints on causes and effects. We show example use cases developed for a commercial application in enterprise risk management.

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30

Mulkar-Mehta, Rutu. "Granular Causality Applications." International Journal of Cognitive Informatics and Natural Intelligence 6, no. 3 (July 2012): 88–108. http://dx.doi.org/10.4018/jcini.2012070105.

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Causal markers, syntactic structures and connectives have been the sole identifying features for automatically extracting causal relations in natural language discourse. However, various connectives such as “and”, prepositions such as “as”, and other syntactic structures are highly ambiguous in nature, as they have multiple meanings besides causality. As a result, one cannot solely rely on lexico-syntactic markers for detection of causal phenomenon in discourse. This paper introduces the Theory of Granular Causality and describes a new approach to identify causality in natural language. Causality is often granular in nature (Mulkar-Mehta, 2011; Mazlack, 2004), and this property of causality is used to discover and infer the presence of causal relations in text. This is compared with causal relations identified using just causal markers. A precision of 0.91 and a recall of 0.79 is achieved using granularity for causal relation detection, as compared to a precision of 0.79 and a recall of 0.44 using text-based causal words for causality detection. Next, the author presents the findings for discovering causal relations between two sentences in an article. The system achieves a precision of 0.60 for discovering causality between two sentences using granular causality markers as features. The results are encouraging, and show that the granular causality is an important phenomenon in natural language

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31

Merker, Bjorn. "Genes, hosts, goals: Disentangling causal dependencies." Behavioral and Brain Sciences 37, no. 2 (April 2014): 150–51. http://dx.doi.org/10.1017/s0140525x13002094.

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AbstractThe special sense in which the concept of “selfishness” is defined in Dawkins's popularization of basic evolutionary theory is analyzed with regard to its applicability to the relation between goals and those who entertain and pursue them. It is concluded that grounds analogous to those on which independent self-interest vis-á-vis their hosts is attributed to genes in Dawkins's sense are lacking in the case of goals in their relation to those who entertain and pursue them.

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32

Felline, Laura. "On Explaining Quantum Correlations: Causal vs. Non-Causal." Entropy 23, no. 5 (May 10, 2021): 589. http://dx.doi.org/10.3390/e23050589.

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At the basis of the problem of explaining non-local quantum correlations lies the tension between two factors: on the one hand, the natural interpretation of correlations as the manifestation of a causal relation; on the other, the resistance on the part of the physics underlying said correlations to adjust to the most essential features of a pre-theoretic notion of causation. In this paper, I argue for the rejection of the first horn of the dilemma, i.e., the assumption that quantum correlations call for a causal explanation. The paper is divided into two parts. The first, destructive, part provides a critical overview of the enterprise of causally interpreting non-local quantum correlations, with the aim of warning against the temptation of an account of causation claiming to cover such correlations ‘for free’. The second, constructive, part introduces the so-called structural explanation (a variety of non-causal explanation that shows how the explanandum is the manifestation of a fundamental structure of the world) and argues that quantum correlations might be explained structurally in the context of an information-theoretic approach to QT.

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Bar-Asher Siegal, Elitzur Avraham, Noa Bassel, and York Hagmayer. "Causal selection – the linguistic take." Experiments in Linguistic Meaning 1 (July 30, 2021): 27. http://dx.doi.org/10.3765/elm.1.4887.

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Causal Selection is a widely discussed topic in philosophy and the cognitive sciences, concerned with characterizing the choice of "the cause" among the many individually necessary and jointly sufficient conditions on which any effect depends on. In this paper, we argue for an additional selection process underlying causal statements: Causative-Construction Selection, which pertains to the choice of linguistic constructions used to express causal relations. By exploring this phenomenon, we aim to answer the following question: given that a speaker wishes to describe the relation between one of the conditions and the effect, which linguistic constructions are available? We take CC-selection to be more crucial than causal selection, since the latter is in fact restricted by the linguistic options resulting from the former. Based on a series of experiments, we demonstrate that factors taken previously as contributing to causal selection should, in fact, be considered as the parameters that license the various linguistic constructions under given circumstances, based on previous knowledge about the causal structure of the world (the causal model). These factors are therefore part of the meaning of the causative expressions.

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34

Guo, Yi, Zhihong Wang, and Zhiqing Shao. "Improving Causality Induction with Category Learning." Scientific World Journal 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/650147.

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Causal relations are of fundamental importance for human perception and reasoning. According to the nature of causality, causality has explicit and implicit forms. In the case of explicit form, causal-effect relations exist at either clausal or discourse levels. The implicit causal-effect relations heavily rely on empirical analysis and evidence accumulation. This paper proposes a comprehensive causality extraction system (CL-CIS) integrated with the means of category-learning. CL-CIS considers cause-effect relations in both explicit and implicit forms and especially practices the relation between category and causality in computation. In elaborately designed experiments, CL-CIS is evaluated together with general causality analysis system (GCAS) and general causality analysis system with learning (GCAS-L), and it testified to its own capability and performance in construction of cause-effect relations. This paper confirms the expectation that the precision and coverage of causality induction can be remarkably improved by means of causal and category learning.

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Chatzimavridou-Grigoriadou, Victoria, Alexander G. Mathioudakis, Alexandru Corlateanu, Georgios A. Mathioudakis, and Efstathia Evangelopoulou. "Obstructive Sleep Apnea and Hypertension: A Bidirectional Causal Relation." Current Respiratory Medicine Reviews 11, no. 4 (December 16, 2015): 272–77. http://dx.doi.org/10.2174/1573398x11666150914212040.

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Fu, Jianfeng, Zongtian Liu, Wei Liu, and Qiang Guo. "Using dual-layer CRFs for event causal relation extraction." IEICE Electronics Express 8, no. 5 (2011): 306–10. http://dx.doi.org/10.1587/elex.8.306.

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Oken, Emily, Izzuddin M. Aris, and Jessica G. Young. "Pre-pregnancy weight and preterm birth: a causal relation?" Lancet Diabetes & Endocrinology 7, no. 9 (September 2019): 663–65. http://dx.doi.org/10.1016/s2213-8587(19)30252-9.

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Oosterkamp, Brand, Kluin-Nelemans, and Vandenbroucke. "Pregnancy and severe aplastic anaemia: causal relation or coincidence?" British Journal of Haematology 103, no. 2 (November 1998): 315–16. http://dx.doi.org/10.1046/j.1365-2141.1998.00978.x.

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Layton, Allan P. "A causal caveat in the Australian money/income relation." Applied Economics 17, no. 2 (April 1, 1985): 263–69. http://dx.doi.org/10.1080/00036848500000023.

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Shalat, Bernard D. Goldstein Stuart L. "The causal relation between benzene exposure and multiple myeloma." Blood 95, no. 4 (February 15, 2000): 1512–13. http://dx.doi.org/10.1182/blood.v95.4.1512.

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Munguía-Calzada, Pablo, Blanca Vivanco, Pedro Oliva-Nacarino, and Jorge Santos-Juanes. "Melanoma, eruptive naevi and natalizumab: Causal relation or coincidence?" Australasian Journal of Dermatology 58, no. 4 (September 19, 2017): 330–31. http://dx.doi.org/10.1111/ajd.12724.

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Wilkinson, J. M., M. R. McClelland, and R. D. Battersby. "Spinal cord schwannoma after vertebral trauma: a causal relation?" Journal of Neurology, Neurosurgery & Psychiatry 59, no. 4 (October 1, 1995): 358. http://dx.doi.org/10.1136/jnnp.59.4.358.

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Pagano, Joseph S., Martin Blaser, Marie-Annick Buendia, Blossom Damania, Kamel Khalili, Nancy Raab-Traub, and Bernard Roizman. "Infectious agents and cancer: criteria for a causal relation." Seminars in Cancer Biology 14, no. 6 (December 2004): 453–71. http://dx.doi.org/10.1016/j.semcancer.2004.06.009.

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Ma, Qichen. "Causal relation extraction and network construction of web events." International Journal of Social and Humanistic Computing 3, no. 2 (2019): 135. http://dx.doi.org/10.1504/ijshc.2019.10023074.

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Ma, Qichen. "Causal relation extraction and network construction of web events." International Journal of Social and Humanistic Computing 3, no. 2 (2019): 135. http://dx.doi.org/10.1504/ijshc.2019.101592.

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Bosch, F. X., A. Lorincz, N. Munoz, C. J. L. M. Meijer, and K. V. Shah. "The causal relation between human papillomavirus and cervical cancer." Journal of Clinical Pathology 55, no. 4 (April 1, 2002): 244–65. http://dx.doi.org/10.1136/jcp.55.4.244.

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Pillai, Rajath Sasidharan, Kiran Iyer, Rubens Spin-Neto, Simple Futarmal Kothari, Jørgen Feldbæk Nielsen, and Mohit Kothari. "Oral Health and Brain Injury: Causal or Casual Relation?" Cerebrovascular Diseases Extra 8, no. 1 (January 9, 2018): 1–15. http://dx.doi.org/10.1159/000484989.

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Background: To systematically review the current literature investigating the association between oral health and acquired brain injury. Methods: A structured search strategy was applied to PubMed, Embase, Web of Science, and CENTRAL electronic databases until March 2017 by 2 independent reviewers. The preferred reporting items for systematic review and meta-analysis guidelines were used for systematic review. Results: Even though the objective was to assess the association between oral health and acquired brain injury, eligible studies focused solely on different forms of stroke and stroke subtypes. Stroke prediction was associated with various factors such as number of teeth, periodontal conditions (even after controlling for confounding factors), clinical attachment loss, antibody levels to Aggregatibacter actinomycetemcomitans and Prevotella intermedia. The literature showed no consensus on the possible association between gingivitis and stroke. Patients with stroke generally had poorer oral hygiene practices and oral health. Dental prophylaxis and professional intervention reduced the incidence of stroke. Conclusions: Overall, oral health and stroke were related. Periodontitis and tooth loss were independently associated with stroke. However, prevention and timely intervention may reduce the risk of stroke. Stroke was the main cerebral lesion studied in the literature, with almost no publications on other brain lesions.

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Kim, Soon-Kwon, Sun-Chul Hwang, and Bum-Tae Kim. "Primary Traumatic Brainstem Hematomas: Consideration for the Causal Relation." Journal of Korean Neurotraumatology Society 6, no. 1 (2010): 18. http://dx.doi.org/10.13004/jknts.2010.6.1.18.

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Santinelli, Vincenzo, Igino Oppo, Crescenzo Materazzi, Aldo Rabuano, Maria Maddalena Piscitelli, Francesco Basile, Mario Palma, and Anna Giunta. "Causal relation between silent myocardial ischemia and sudden death." American Heart Journal 128, no. 4 (October 1994): 816–20. http://dx.doi.org/10.1016/0002-8703(94)90281-x.

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Modan, B. "Diet and cancer: causal relation or just wishful thinking?" Lancet 340, no. 8812 (July 1992): 162–64. http://dx.doi.org/10.1016/0140-6736(92)93227-e.

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