Journal articles: 'Knowledge and causality'

1

Casullo, Albert. "Causality, Reliabilism, and Mathematical Knowledge." Philosophy and Phenomenological Research 52, no. 3 (September 1992): 557. http://dx.doi.org/10.2307/2108208.

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KAYA, Emrah. "İbn Haldûn’un Nedensellik ve Rasyonel Bilgi Düşüncesine Eleştirel Bir Yaklaşım." ULUM 3, no. 2 (December 31, 2020): 241–61. http://dx.doi.org/10.54659/ulum.798914.

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The purpose of this study is to critically approach the thoughts of causality and rational knowledge in Ibn Khaldūn, who is one of the greatest names of Islamic philosophy. Ibn Khaldūn, who is a tremendously competent sociologist, historian, and politician, constituted his work entitled Muqaddima in a way exhibiting the science of ʿumrān. One of the fundamentals of science undoubtedly is the theory of causality. We see that Ibn Khaldūn, who construed everything in the universe in the light of the causality, does not use the same theory when miracles and supernatural events are in question. This differentiation basing on the distinction of the human intellect and divine revelation has not eliminated any contradiction coming out in the context of the causality. Another matter we examine in this study is the critique of Ibn Khaldūn about rational knowledge against philosophers. According to him, it is not a correct method reaching the universals with abstractions made from the particulars. It is because such universals have not been compatible with the facts. Ibn Khaldūn criticizes the philosophers in the context of metaphysical knowledge. But, if we consider the science of ʿumrān to be a kind of metaphysics, we might say that his method contains some contradictions.

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Bénichou, Christian. "Medical Knowledge: The Essential of Causality Assessment." Drug Information Journal 29, no. 1 (January 1995): 315–18. http://dx.doi.org/10.1177/009286159502900136.

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Pechsiri, Chaveevan, and Asanee Kawtrakul. "Mining Causality for Explanation Knowledge from Text." Journal of Computer Science and Technology 22, no. 6 (November 2007): 877–89. http://dx.doi.org/10.1007/s11390-007-9093-8.

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Wu, Sifan, Ruihui Zhao, Yefeng Zheng, Jian Pei, and Bang Liu. "Identify Event Causality with Knowledge and Analogy." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 11 (June 26, 2023): 13745–53. http://dx.doi.org/10.1609/aaai.v37i11.26610.

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Event causality identification (ECI) aims to identify the causal relationship between events, which plays a crucial role in deep text understanding. Due to the diversity of real-world causality events and difficulty in obtaining sufficient training data, existing ECI approaches have poor generalizability and struggle to identify the relation between seldom seen events. In this paper, we propose to utilize both external knowledge and internal analogy to improve ECI. On the one hand, we utilize a commonsense knowledge graph called ConceptNet to enrich the description of an event sample and reveal the commonalities or associations between different events. On the other hand, we retrieve similar events as analogy exam- ples and glean useful experiences from such analogous neigh- bors to better identify the relationship between a new event pair. By better understanding different events through exter- nal knowledge and making an analogy with similar events, we can alleviate the data sparsity issue and improve model gener- alizability. Extensive evaluations on two benchmark datasets show that our model outperforms other baseline methods by around 18% on the F1-value on average

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Herrero, J. C., and J. Mira. "Causality levels in SCHEMA: A knowledge edition interface." IEE Proceedings - Software 147, no. 5 (2000): 193. http://dx.doi.org/10.1049/ip-sen:20000900.

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Canali, Stefano. "Big Data, epistemology and causality: Knowledge in and knowledge out in EXPOsOMICS." Big Data & Society 3, no. 2 (September 22, 2016): 205395171666953. http://dx.doi.org/10.1177/2053951716669530.

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Usó-Domenech, Jose-Luis, Josué Antonio Nescolarde-Selva, and Miguel Lloret-Climent. "Causality in complex systems." Kybernetes 46, no. 4 (April 3, 2017): 590–602. http://dx.doi.org/10.1108/k-08-2016-0195.

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Purpose The purpose of this paper is the study of the causal relationship. The concept called “naive” causality can be stated more generally as the belief (or knowledge) that results follow actions, and that these results are not random, but are consistently linked with causes. The authors have thus formed a very general and precarious concept of causality, but one that appropriately reflects the meaning of causality at the level of common sense. Design/methodology/approach Mathematical and logical development of the causality in complex systems. Findings There are three aspects of rationality that give the human mind a unique vision of reality: quantification: reduction of phenomena to quantitative terms; cause and effect: causal relationship, which allows predicting; and the necessary and valid use of (deterministic) mechanical models. This work is dedicated to the second aspect, that of causality, but at present leaves aside the discussion of possibility-necessity, proposing a modification to philosophical synthesis of causality specified by Bunge (1959), with contributions made by Patten et al. (1976) and LeShan and Margenau (1982). Originality/value Causality is an epistemological category, because it concerns the experience and knowledge of the human subject, without being necessarily a property of reality.

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Niu, Guanglin, and Bo Li. "Logic and Commonsense-Guided Temporal Knowledge Graph Completion." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 4 (June 26, 2023): 4569–77. http://dx.doi.org/10.1609/aaai.v37i4.25579.

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A temporal knowledge graph (TKG) stores the events derived from the data involving time. Predicting events is extremely challenging due to the time-sensitive property of events. Besides, the previous TKG completion (TKGC) approaches cannot represent both the timeliness and the causality properties of events, simultaneously. To address these challenges, we propose a Logic and Commonsense-Guided Embedding model (LCGE) to jointly learn the time-sensitive representation involving timeliness and causality of events, together with the time-independent representation of events from the perspective of commonsense. Specifically, we design a temporal rule learning algorithm to construct a rule-guided predicate embedding regularization strategy for learning the causality among events. Furthermore, we could accurately evaluate the plausibility of events via auxiliary commonsense knowledge. The experimental results of TKGC task illustrate the significant performance improvements of our model compared with the existing approaches. More interestingly, our model is able to provide the explainability of the predicted results in the view of causal inference. The appendix, source code and datasets of this paper are available at https://github.com/ngl567/LCGE.

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Stearns, Justin. "“All Beneficial Knowledge is Revealed”:." islamic law and society 21, no. 1-2 (January 30, 2014): 49–80. http://dx.doi.org/10.1163/15685195-02112p02.

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The intellectual history of the Muslim world during the post-formative period is poorly understood compared to the centuries in which the initial development of the principal Islamic intellectual traditions occurred. This article examines the legal status of the natural sciences in the thought of the Moroccan scholar al-Ḥasan al-Yūsī (d. 1102/1691) and his contemporaries, both in terms of the categorization of knowledge and in terms of developments in conceptions of causality in post-formative Ashʿarī theology. In the latter respect, al-Yūsī’s writings on causality are compared to those of his contemporary in Damascus, ʿAbd al-Ghanī al-Nābulusī, with attention to the broader historiographic perils in comparing intellectual developments in the Early Modern period to those occurring in Europe. By placing al-Yūsī’s views in intellectual context, I seek to demonstrate how a more productive history of the natural sciences in the post-formative Muslim world might be written.

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Yu, Hong Qing, and Stephan Reiff-Marganiec. "Learning Disease Causality Knowledge From the Web of Health Data." International Journal on Semantic Web and Information Systems 18, no. 1 (January 2022): 1–19. http://dx.doi.org/10.4018/ijswis.297145.

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Health information becomes importantly valuable for protecting public health in the current coronavirus situation. Knowledge-based information systems can play a crucial role in helping individuals to practice risk assessment and remote diagnosis. We introduce a novel approach that will develop causality-focused knowledge learning in a robust and transparent manner. Then, the machine gains the causality and probability knowledge for inference (thinking) and accurate prediction later. Besides, the hidden knowledge can be discovered beyond the existing understanding of the diseases. The whole approach is built on a Causal Probability Description Logic Framework that combines Natural Language Processing (NLP), Causality Analysis and extended Knowledge Graph (KG) technologies together. The experimental work has processed 801 diseases in total (from the UK NHS website linking with DBpedia datasets). As a result, the machine learnt comprehensive health causal knowledge and relations among the diseases, symptoms, and other facts efficiently.

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Haeri, Seyed Hossein, Peter Van Roy, Carlos Baquero, and Christopher Meiklejohn. "Worlds of Events: Deduction with Partial Knowledge about Causality." Electronic Proceedings in Theoretical Computer Science 223 (August 10, 2016): 113–27. http://dx.doi.org/10.4204/eptcs.223.8.

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Seo, Wonchul. "Analyzing Causality of Technological Knowledge Spillovers: Patent Analysis Approach." Procedia Manufacturing 2 (2015): 485–89. http://dx.doi.org/10.1016/j.promfg.2015.07.083.

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Pechsiri, Chaveevan, and Rapepun Piriyakul. "Explanation Knowledge Graph Construction Through Causality Extraction from Texts." Journal of Computer Science and Technology 25, no. 5 (September 2010): 1055–70. http://dx.doi.org/10.1007/s11390-010-9387-0.

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Kim, Euhee, and Sunjoo Choi. "On The L2 LSTM LM’s Knowledge of Implicit Causality." Korean Journal of Applied Linguistics 39, no. 4 (December 31, 2023): 107–24. http://dx.doi.org/10.17154/kjal.2023.12.39.4.107.

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KAYA, Emrah. "A Critical Approach to Causality and Rational Knowledge in Ibn Khaldūn." ULUM 3, no. 2 (December 31, 2020): 411–31. http://dx.doi.org/10.54659/ulum.837132.

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The purpose of this study is to critically approach the thoughts of causality and rational knowledge in Ibn Khaldūn, who is one of the greatest names of Islamic philosophy. Ibn Khaldūn, who is a tremendously competent sociologist, historian, and politician, constituted his work entitled Muqaddima in a way exhib-iting the science of ʿumrān. One of the fundamentals of science undoubtedly is the theory of causality. We see that Ibn Khaldūn, who construed everything in the universe in the light of the causality, does not use the same theory when miracles and supernatural events are in question. This differentiation basing on the distinction of the human intellect and divine revelation has not eliminated any contradiction coming out in the context of the causality. Another matter we examine in this study is the critique of Ibn Khal-dūn about rational knowledge against philosophers. According to him, it is not a correct method reaching the universals with abstractions made from the particulars. It is because such universals have not been compatible with the facts. Ibn Khaldūn criticizes the philosophers in the context of metaphysical knowledge. But, if we consider the science of ʿumrān to be a kind of metaphysics, we might say that his method contains some contradictions.

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Singh, Raghuveer. "Causality, Meaning and Purpose in Politics." Review of Politics 47, no. 3 (July 1985): 390–410. http://dx.doi.org/10.1017/s0034670500036937.

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Political science is in a state of crisis today. The crisis is the result of the scientistic predicament. Man has become the victim of his own reason and knowledge. Scientific rationality and value-neutral theories of knowledge lead to the eclipse of the public realm and the growth of social regimentation, mass manipulation, large-scale indoctrination and totalitarian domination. As a result, the homo politicus is reduced to the homo faber and the animal laboran. What is required is a radical shift in our intellectual perspective. Phenomenological and linguistic-analytical theories of action are inadequate to provide a sound basis for political science. Philosphia perennis alone can restore to politics its full glory and splendor.

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Sarin, Arunima, David A. Lagnado, and Paul W. Burgess. "The Intention-Outcome Asymmetry Effect." Experimental Psychology 64, no. 2 (March 2017): 124–41. http://dx.doi.org/10.1027/1618-3169/a000359.

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Abstract. Knowledge of intention and outcome is integral to making judgments of responsibility, blame, and causality. Yet, little is known about the effect of conflicting intentions and outcomes on these judgments. In a series of four experiments, we combine good and bad intentions with positive and negative outcomes, presenting these through everyday moral scenarios. Our results demonstrate an asymmetry in responsibility, causality, and blame judgments for the two incongruent conditions: well-intentioned agents are regarded more morally and causally responsible for negative outcomes than ill-intentioned agents are held for positive outcomes. This novel effect of an intention-outcome asymmetry identifies an unexplored aspect of moral judgment and is partially explained by extra inferences that participants make about the actions of the moral agent.

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Fenker, Daniela B., Mircea A. Schoenfeld, Michael R. Waldmann, Hartmut Schuetze, Hans-Jochen Heinze, and Emrah Duezel. "“Virus and Epidemic”: Causal Knowledge Activates Prediction Error Circuitry." Journal of Cognitive Neuroscience 22, no. 10 (October 2010): 2151–63. http://dx.doi.org/10.1162/jocn.2009.21387.

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Knowledge about cause and effect relationships (e.g., virus–epidemic) is essential for predicting changes in the environment and for anticipating the consequences of events and one's own actions. Although there is evidence that predictions and learning from prediction errors are instrumental in acquiring causal knowledge, it is unclear whether prediction error circuitry remains involved in the mental representation and evaluation of causal knowledge already stored in semantic memory. In an fMRI study, participants assessed whether pairs of words were causally related (e.g., virus–epidemic) or noncausally associated (e.g., emerald–ring). In a second fMRI study, a task cue prompted the participants to evaluate either the causal or the noncausal associative relationship between pairs of words. Causally related pairs elicited higher activity in OFC, amygdala, striatum, and substantia nigra/ventral tegmental area than noncausally associated pairs. These regions were also more activated by the causal than by the associative task cue. This network overlaps with the mesolimbic and mesocortical dopaminergic network known to code prediction errors, suggesting that prediction error processing might participate in assessments of causality even under conditions when it is not explicitly required to make predictions.

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Adekayanti, Aida Asti, Handry Sudiartha Athar, and Lalu M. Furkan. "The Effect of Subjective Knowledge, Objective Knowledge, and Experience Knowledge on Interest in Buying British Propolis Products." International Journal of Multicultural and Multireligious Understanding 9, no. 2 (February 3, 2022): 166. http://dx.doi.org/10.18415/ijmmu.v9i2.3368.

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This study aims to determine how the influence of subjective knowledge, objective knowledge, and experience knowledge on buying interest in British Propolis products in West Nusa Tenggara. The type of research used is quantitative research with causality association approach. The population in this study are consumers who know British Propolis and have consumed British in West Nusa Tenggara, with an unknown population. The number of samples taken were 100 consumers aged ≤25 - ≥40 years. The sampling technique used was purposive sampling technique. Data analysis used multiple linear regression analysis with SPSS 2.5 for windows application. The results showed that subjective knowledge had no effect on interest. While objective knowledge.

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MAYOCCHI, Enrique Santiago. "Efficient Causality in the Actual Intellectual Knowledge According to John Duns Scotus." Revista Española de Filosofía Medieval 24 (November 24, 2017): 139. http://dx.doi.org/10.21071/refime.v24i.10456.

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The subject of causality appears in many of the solutions proposed by Duns Scotus on various philosophical problems, such as voluntary act, and theological problems, as the divine dispensation of grace in the sacraments. This paper shows the kinds of causes and causality which are involved in the actual act of intellection. It focuses on the concept of essential order as the source of the different kinds of causal concurrence, and applies this concept to the act of actual intellection, interpreting it according to the idea of unitas ordinis.

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Bonnefon, Jean-François, Rui Da Silva Neves, Didier Dubois, and Henri Prade. "Predicting causality ascriptions from background knowledge: model and experimental validation." International Journal of Approximate Reasoning 48, no. 3 (August 2008): 752–65. http://dx.doi.org/10.1016/j.ijar.2007.07.003.

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Argyris, Chris. "Actionable Knowledge: Design Causality in the Service of Consequential Theory." Journal of Applied Behavioral Science 32, no. 4 (December 1996): 390–406. http://dx.doi.org/10.1177/0021886396324004.

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Chen, Siyuan, and Kezhi Mao. "Explicit and implicit knowledge-enhanced model for event causality identification." Expert Systems with Applications 238 (March 2024): 122039. http://dx.doi.org/10.1016/j.eswa.2023.122039.

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POLYAK, STEPHEN T., and AUSTIN TATE. "Rationale in planning: causality, dependencies, and decisions." Knowledge Engineering Review 13, no. 3 (November 1998): 247–62. http://dx.doi.org/10.1017/s026988899800201x.

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Traditional approaches to plan representation have focused on the generation of a sequence of actions and orderings. Knowledge rich models, which incorporate plan rationale, provide benefits to the planning process in a number of ways. The use of rationale in planning is reviewed in terms of causality, dependencies, and decisions. Each dimension addresses practical issues in the planning process, and adds value to the resultant plan. The contribution of this paper is to explore this categorisation, and to motivate the need to explicitly record and represent rationale knowledge for situated, mixed-initiative planning systems.

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Radinsky, K., S. Davidovich, and S. Markovitch. "Learning to Predict from Textual Data." Journal of Artificial Intelligence Research 45 (December 26, 2012): 641–84. http://dx.doi.org/10.1613/jair.3865.

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Given a current news event, we tackle the problem of generating plausible predictions of future events it might cause. We present a new methodology for modeling and predicting such future news events using machine learning and data mining techniques. Our Pundit algorithm generalizes examples of causality pairs to infer a causality predictor. To obtain precisely labeled causality examples, we mine 150 years of news articles and apply semantic natural language modeling techniques to headlines containing certain predefined causality patterns. For generalization, the model uses a vast number of world knowledge ontologies. Empirical evaluation on real news articles shows that our Pundit algorithm performs as well as non-expert humans.

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Wuryaningrat, Nikolas F., Paulus Kindangen, and Ardianus L. Paulus. "Trust as a Key Factor in Knowledge Transfer and Innovation Capabilities." SHS Web of Conferences 149 (2022): 02023. http://dx.doi.org/10.1051/shsconf/202214902023.

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The era of knowledge-based economy has created rapid changes in the business environment. Maintaining a business in that era requires innovation to maintaining its competitiveness. Innovation requires knowledge resources, where knowledge transfer plays an essential role in creating new knowledge that can be utilized to improve innovation capabilities. However, knowledge transfer is considered not a simple process because of the stickiness of knowledge, but on the other hand, knowledge transfer can be regarded as something that can happen instantly. Trust is therefore considered as the factor to strengthening the causality relationship between knowledge transfer and innovation capabilities. This study used a survey approach with Partially Least Square (PLS) data analysis techniques. The respondents in this research are SMEs of the creative industry handicraft sub-sector in the Province of DIY, Bali and North Sulawesi, which are justified as representations of Indonesia. Based on the 201 data collected, it was found that trust significantly moderated the causality relationship between knowledge transfer and innovation capabilities. Hence, knowledge transfers are needed for developing SMEs of the creative industries innovation capabilities which strengthened by the trust.

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Osoba, Osonde A., and Bart Kosko. "Fuzzy cognitive maps of public support for insurgency and terrorism." Journal of Defense Modeling and Simulation: Applications, Methodology, Technology 14, no. 1 (January 2017): 17–32. http://dx.doi.org/10.1177/1548512916680779.

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Feedback fuzzy cognitive maps (FCMs) can model the complex structure of public support for insurgency and terrorism (PSOT). FCMs are fuzzy causal signed digraphs that model degrees of causality in interwoven webs of feedback causality and policy variables. Their nonlinear dynamics permit forward-chaining inference from input causes and policy options to output effects. We show how a concept node causally affects downstream nodes through a weighted product of the intervening causal edge strengths. FCMs allow users to add detailed dynamics and feedback links directly to the causal model. Users can also fuse or combine FCMs from multiple experts by weighting and adding the underlying FCM fuzzy edge matrices. The combined FCM tends to better represent domain knowledge as the expert sample size increases if the expert sample approximates a random sample. Statistical or machine-learning algorithms can use numerical sample data to learn and tune a FCM’s causal edges. A differential Hebbian learning law can approximate a PSOT FCM’s directed edges of partial causality using time-series training data. The PSOT FCM adapts to the computational factor-tree PSOT model that Davis and OMahony based on prior social science research and case studies. Simulation experiments compare the PSOT models with the adapted FCM models.

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Suyudi, M., and Wahyu Hanafi Putra. "Kritik Nalar Kausalitas dan Pengetahuan David Hume." Al-Adabiya: Jurnal Kebudayaan dan Keagamaan 15, no. 02 (November 21, 2020): 201–14. http://dx.doi.org/10.37680/adabiya.v15i02.569.

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This research aims at explaining David Hume’s logical critique of causality and knowledge. As library research, the method used is descriptive-qualitative. Data and data sources were obtained from his important works Why Cause is Always A Need and A Treatise of Human Nature and several secondary literatures on causality. The data was carried out through documentation, started by the researcher documenting Hume's thoughts, especially criticism of the law of causality (cause-effect) and knowledge of both of Hume's primary works. The study results explained that Hume criticized the performance of the law of causality, which explained that the existence of a second essence and after it was an impact or certainty of the first essence. The second essential is the consequence and legitimacy of the first one. According to Hume, it cannot serve empirically as the law of causality occurs because the sequential process is stagnant. Hume's skepticism and doubts over dogmatic and metaphysical matters then affect that all knowledge can only be explored with the five senses and is empirical. All irrational and non-empirical characteristics cannot be attributed to a belief and truth. In conclusion, real truths in knowledge are those that can be investigated empirically. Keywords: Causality, Hume, Knowledge, The five senses. Penelitian ini bertujuan menjelaskan kritik nalar kausalitas dan pengetahuan David Hume. Sebagai penelitian pustaka, metode yang digunakan adalah deskriptif-kualitatif. Data dan sumber data didapat dari karya-karya Why Cause is Always Necessary dan A Treatise of Human Nature serta literatur-literatur sekunder yang berkaitan dengan tema kausalitas. Teknik pengumpulan data dilakukan dengan dokumentasi, yaitu peneliti mendokumentasikan pemikiran-pemikiran Hume terutama kritik atas hukum kausalitas (sebab-akibat) dan pengetahuan dari kedua karya primer Hume tersebut. Hasil penelitian menjelaskan bahwa Hume melakukan kritik atas kinerja hukum kausalitas yang menjelaskan bahwa adanya esensi kedua dan setelahnya merupakan dampak atau keniscayaan atas esensi pertama. Esensi kedua merupakan akibat dan legitimasi dari esensi pertama. Hal demikian yang menurut Hume tidak dapat dijelaskan secara empiris. Menurutnya, hukum kausalitas itu terjadi karena proses keterurutan secara stagnan. Sikap skeptis dan ragu-ragu Hume atas perihal yang sifatnya dogmatis dan metafisik membawa dampak bahwa segala pengetahuan hanya bisa digali dengan panca inderawi dan bersifat empiris. Semua perihal yang sifatnya irasional dan tidak empiris tidak dapat dinisbatkan pada suatu keyakinan dan kebenaran. Pada akhirnya, kebenaran sejati dalam pengetahuan adalah yang dapat diselidiki secara empiris. Kata kunci: Hume, Kausalitas, Pengetahuan, Panca Indera

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Karimov, Mehman, and Davit Belkania. "A Case Study of Foreign Direct Investment and Economic Growth Relationship in Turkey." European Journal of Marketing and Economics 1, no. 3 (November 29, 2018): 97. http://dx.doi.org/10.26417/ejme.v1i3.p97-101.

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Foreign direct investment is believed to enhance long-term economic growth of a country through knowledge spillovers and technology transfers. This paper is an empirical attempt to check the effects of the foreign direct investment (FDI) on the economic growth (GDP) of Turkey. The paper uses time span from 1980 to 2017 for statistical analysis. Johansen co-integration and Granger causality tests were applied for empirical analysis. The results of the tests confirmed the presence of the co-integration between GDP and FDI as it was expected from the beginning. Furthermore, Granger causality test showed the unidirectional causality from FDI to GDP.

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Xu, Liyang, and Dezheng Wang. "The reconstruction of equivalent underlying model based on direct causality for multivariate time series." PeerJ Computer Science 10 (March 18, 2024): e1922. http://dx.doi.org/10.7717/peerj-cs.1922.

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This article presents a novel approach for reconstructing an equivalent underlying model and deriving a precise equivalent expression through the use of direct causality topology. Central to this methodology is the transfer entropy method, which is instrumental in revealing the causality topology. The polynomial fitting method is then applied to determine the coefficients and intrinsic order of the causality structure, leveraging the foundational elements extracted from the direct causality topology. Notably, this approach efficiently discovers the core topology from the data, reducing redundancy without requiring prior domain-specific knowledge. Furthermore, it yields a precise equivalent model expression, offering a robust foundation for further analysis and exploration in various fields. Additionally, the proposed model for reconstructing an equivalent underlying framework demonstrates strong forecasting capabilities in multivariate time series scenarios.

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Perry, Michelle, and Anastasia Danos Elder. "Knowledge in transition: Adults' developing understanding of a principle of physical causality." Cognitive Development 12, no. 1 (January 1997): 131–57. http://dx.doi.org/10.1016/s0885-2014(97)90033-2.

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Zhang, Qin. "Dynamic Uncertain Causality Graph for Knowledge Representation and Reasoning: Discrete DAG Cases." Journal of Computer Science and Technology 27, no. 1 (January 2012): 1–23. http://dx.doi.org/10.1007/s11390-012-1202-7.

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GRANEROD, J., R. CUNNINGHAM, M. ZUCKERMAN, K. MUTTON, N. W. S. DAVIES, A. L. WALSH, K. N. WARD, et al. "Causality in acute encephalitis: defining aetiologies." Epidemiology and Infection 138, no. 6 (April 14, 2010): 783–800. http://dx.doi.org/10.1017/s0950268810000725.

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SUMMARYDefining the causal relationship between a microbe and encephalitis is complex. Over 100 different infectious agents may cause encephalitis, often as one of the rarer manifestations of infection. The gold-standard techniques to detect causative infectious agents in encephalitis in life depend on the study of brain biopsy material; however, in most cases this is not possible. We present the UK perspective on aetiological case definitions for acute encephalitis and extend them to include immune-mediated causes. Expert opinion was primarily used and was supplemented by literature-based methods. Wide usage of these definitions will facilitate comparison between studies and result in a better understanding of the causes of this devastating condition. They provide a framework for regular review and updating as the knowledge base increases both clinically and through improvements in diagnostic methods. The importance of new and emerging pathogens as causes of encephalitis can be assessed against the principles laid out here.

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Papaspyropoulos, Konstantinos G., and Dimitris Kugiumtzis. "On the Validity of Granger Causality for Ecological Count Time Series." Econometrics 12, no. 2 (May 9, 2024): 13. http://dx.doi.org/10.3390/econometrics12020013.

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Knowledge of causal relationships is fundamental for understanding the dynamic mechanisms of ecological systems. To detect such relationships from multivariate time series, Granger causality, an idea first developed in econometrics, has been formulated in terms of vector autoregressive (VAR) models. Granger causality for count time series, often seen in ecology, has rarely been explored, and this may be due to the difficulty in estimating autoregressive models on multivariate count time series. The present research investigates the appropriateness of VAR-based Granger causality for ecological count time series by conducting a simulation study using several systems of different numbers of variables and time series lengths. VAR-based Granger causality for count time series (DVAR) seems to be estimated efficiently even for two counts in long time series. For all the studied time series lengths, DVAR for more than eight counts matches the Granger causality effects obtained by VAR on the continuous-valued time series well. The positive results, also in two ecological time series, suggest the use of VAR-based Granger causality for assessing causal relationships in real-world count time series even with few distinct integer values or many zeros.

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AGNEW, JULIE R., LISA R. SZYKMAN, STEPHEN P. UTKUS, and JEAN A. YOUNG. "Trust, plan knowledge and 401(k) savings behavior." Journal of Pension Economics and Finance 11, no. 1 (May 18, 2011): 1–20. http://dx.doi.org/10.1017/s1474747211000230.

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AbstractPlan knowledge and trust in financial institutions – two variables missing from standard neoclassical or behavioral models of decision-making – are strongly correlated to 401(k) savings behavior based on results from this paper. In voluntary enrollment settings, plan knowledge and demographic characteristics are related to participation in a 401(k) plan. In automatic enrollment settings, trust in financial institutions and knowledge of an available plan match are related to participation. Although this study cannot prove causality of the relationships, it does extend our understanding of the complex factors underlying savings choices. Policy implications are discussed.

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Mayzel, M., K. Kazimierczuk, and V. Yu Orekhov. "The causality principle in the reconstruction of sparse NMR spectra." Chem. Commun. 50, no. 64 (2014): 8947–50. http://dx.doi.org/10.1039/c4cc03047h.

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Yang, Yang, and Yifan Huang. "Knowledge Modeling of power grid regulation based on reasoning map." Journal of Physics: Conference Series 2087, no. 1 (November 1, 2021): 012097. http://dx.doi.org/10.1088/1742-6596/2087/1/012097.

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Abstract To contribute to the intelligence and knowledge of power grid regulation and control operations, this paper presents a method of power grid regulation knowledge modeling based on ELG (Event Logic Graph), which includes an event word extraction based on a predicate-argument model, an event chain extraction and fusion based on event similarity theory, an event generalization based on a soft-pattern algorithm, and an event relationship recognition based on rule pattern matching method and joint constraints. Finally, this paper uses events as nodes and event relationships as directed edges to construct an affair graph stipulated by the power grid regulation and control regulations. The ELG is also called the new generation knowledge graph. But the knowledge graph can only describe the existence of entities and the upper and lower associations between entities. ELG can explain the inheritance, causality between entities and the logic of affair evolution, and the probability of transition between legacy and causality. Therefore, knowledge modeling based on ELG has intelligent advantages. Also, compared with ontology-based knowledge modeling methods, the method proposed in this paper can realize the dynamic representation of control operation knowledge, can express the logic of behavior and logic of operation, and also has higher retrieval accuracy.

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Valsiner, Jaan. "Beyond Compartmentalization: Generalizing Clinical Knowledge in Psychology." RIVISTA DI PSICOLOGIA CLINICA, no. 2 (January 2023): 92–102. http://dx.doi.org/10.3280/rpc2-2022oa14892.

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I expand the efforts to overcome compartmentalization of clinical psychology by reversing the notion of causality to that of resistance, and specify the structure of such resistance. Clinical practices produce psychological knowledge of general kind that leads to the adoption of the basic world view of idiographic science as the basic framework for systemic analysis of generic cases and thus feeds forward to further improvement of the clinical practices. Three directions for the future are outlined: clinical psychology builds on the systemic efforts of idiographic science, used historically structured non-random sampling of lived-through experiences, and situates its generalized knowledge within life-course developmental perspectives.

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VAN DEN HOVEN, EMIEL, and EVELYN C. FERSTL. "Discourse context modulates the effect of implicit causality on rementions." Language and Cognition 10, no. 4 (October 2, 2018): 561–94. http://dx.doi.org/10.1017/langcog.2018.17.

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abstractCertain verbs tend to elicit explanations about either their subject or their object. The tendency for one of the verb’s arguments to be rementioned in explanations is known as the implicit causality bias. In this paper we investigate the conditions underlying implicit causality remention biases by means of sentence and story completion studies. On one account of implicit causality, remention biases are the product of a combination of a particular lexico-semantic structure with a causal coherence relation. According to a competing account, the biases arise from a perceived lack of information in the discourse, and thus depend on knowledge about the world and the discourse context. To distinguish between the two accounts, it first needs to be established that information that potentially competes with implicit causality, such as relevant information from the discourse context, can reliably influence remention biases. We provide evidence that a violation of implicit assumptions underlying the standard use of implicit causality verbs leads to different inferences, and an alteration of the remention bias. We thereby lay the groundwork for future studies to distinguish between the two accounts.

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Waghen, Kerelous, and Mohamed-Salah Ouali. "A Data-Driven Fault Tree for a Time Causality Analysis in an Aging System." Algorithms 15, no. 6 (May 24, 2022): 178. http://dx.doi.org/10.3390/a15060178.

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This paper develops a data-driven fault tree methodology that addresses the problem of the fault prognosis of an aging system based on an interpretable time causality analysis model. The model merges the concepts of knowledge discovery in the dataset and fault tree to interpret the effect of aging on the fault causality structure over time. At periodic intervals, the model captures the cause–effect relations in the form of interpretable logic trees, then represents them in one fault tree model that reflects the changes in the fault causality structure over time due to the system aging. The proposed model provides a prognosis of the probability for fault occurrence using a set of extracted causality rules that combine the discovered root causes over time in a bottom-up manner. The well-known NASA turbofan engine dataset is used as an illustrative example of the proposed methodology.

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Choong, Heng Jie, Eun-jin Kim, and Fei He. "Causality Analysis with Information Geometry: A Comparison." Entropy 25, no. 5 (May 16, 2023): 806. http://dx.doi.org/10.3390/e25050806.

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The quantification of causality is vital for understanding various important phenomena in nature and laboratories, such as brain networks, environmental dynamics, and pathologies. The two most widely used methods for measuring causality are Granger Causality (GC) and Transfer Entropy (TE), which rely on measuring the improvement in the prediction of one process based on the knowledge of another process at an earlier time. However, they have their own limitations, e.g., in applications to nonlinear, non-stationary data, or non-parametric models. In this study, we propose an alternative approach to quantify causality through information geometry that overcomes such limitations. Specifically, based on the information rate that measures the rate of change of the time-dependent distribution, we develop a model-free approach called information rate causality that captures the occurrence of the causality based on the change in the distribution of one process caused by another. This measurement is suitable for analyzing numerically generated non-stationary, nonlinear data. The latter are generated by simulating different types of discrete autoregressive models which contain linear and nonlinear interactions in unidirectional and bidirectional time-series signals. Our results show that information rate causalitycan capture the coupling of both linear and nonlinear data better than GC and TE in the several examples explored in the paper.

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Haggarty, Paul. "B-vitamins, genotype and disease causality." Proceedings of the Nutrition Society 66, no. 4 (October 25, 2007): 539–47. http://dx.doi.org/10.1017/s0029665107005861.

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Despite a great deal of research effort there is still considerable uncertainty surrounding the importance of the B-vitamins in health and disease. This continuing uncertainty is partly a result of the difficulty of measuring intake, confounding in observational studies and the very large numbers required to evaluate primary prevention in randomised controlled trials. Consequently, genetic data are increasingly being used to infer nutritional effects on health and even in the formulation of nutrition policy using the approach of ‘mendelian randomisation’. Genetic information has already contributed greatly to the understanding of B-vitamin metabolism and the heterogeneity of responses to intake. It has the potential to provide further nutritional insights and to assist in the elucidation of causal mechanisms, but it is important that genetic data is not viewed as an alternative to nutritional information, both are necessary when addressing nutritional problems. Similarly, the interpretation of nutrient and biomarker status in some experimental designs may require knowledge of genotype. Formal tests of gene–gene and gene–nutrient interaction are of limited value in nutritional studies and the formulation of policy. Graphical representation of diet–genotype–health data greatly assists in the elucidation of the nature of genetic effects, their interaction with nutrition and the implications for nutrition policy.

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Westphal, Kenneth R. "Noumenal Causality Reconsidered: Affection, Agency, and Meaning in Kant." Canadian Journal of Philosophy 27, no. 2 (June 1997): 209–45. http://dx.doi.org/10.1080/00455091.1997.10717478.

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The lead question of Kant's first Critique, indeed his whole Critical Philosophy is ‘How is Metaphysics as a Science Possible?’ Neo-Kantian and recent Anglophone interpretations of Kant's epistemology have concentrated on the ‘Transcendental Analytic’ of the first Critique, and have taken Kant's positive and legitimate sense of metaphysics to concern the necessary conditions of our knowledge of mathematics, natural science, and of course, our common sense knowledge of a spatio-temporal world of objects and events. However, in the ‘Canon of Pure Reason’ in the first Critique Kant indicates quite clearly that, although two of the leading sub-questions of metaphysics — ‘What should I so?’ and ‘What may I hope?’ — cannot be answered on theoretical grounds, they may be answered on practical grounds (A804-05=B832-33). Those practical grounds are elaborated and supplemented (mainly) in the latter two Critiques and the Religion. In each case, however, a definite and positive answer to a metaphysical question involves giving ‘objective reality’ to a concept, e.g., the concepts of freedom or immortality. ‘Objective reality’ involves possible reference to an object, where ‘possible reference’ involves more than merely describing a logical possibility.

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Nafti, Sawssen. "FINANCIAL DEVELOPMENT, ENVIRONMENTAL QUALITY, TRADE OPENNESS AND ECONOMIC GROWTH: EMPIRICAL STUDY IN MENA COUNTRIES." International Journal of Advanced Research 9, no. 09 (September 30, 2021): 920–31. http://dx.doi.org/10.21474/ijar01/13504.

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In this paper, we empirically investigate the causal relationship between financial development, environmental degradation (CO2 emissions), trade openness and economic growth (GDP), using Panel data (the theory of cointegration Pedroni (1999,2004)) for 12 MENA countries ( Middle East and North Africa) during the period 1990- 2014.The long-term relationships estimated by the modified least squares technique proposed by Pedroni (1996). Our results indicate that there is evidence for a bidirectionel causality between CO2 emissions and economic growth. Economic growth and trade openness are interdependent, it exist a bidirectionel causality. Also, we confirm a bidirectional causality among trade openness and financial development. The unidirectional causality of financial development on economic growth and openness to CO2 emissions trading is identified. Our empirical results also verified the existence of the environmental Kuznets curve hypothesis by the causal link between GDP and environmental polution. Finally, panel causality verifies the existance of bidirectional relationship between economic growth(GDP), environmental degradation(CO2 emissions), financial development and trade openness. This empirical knowledge is of particular interest to policy makers as it helps us create sound economic policies to support economic development and improve environmental quality.

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Thuy Ho, Ngoc, Wann Yi Wu, Adriana Amaya Rivas, and Phuoc Thien Nguyen. "THE CAUSAL RELATIONSHIP BETWEEN GDP, ENERGY CONSUMPTION, POPULATION, AND OIL PRICE: EVIDENCE FROM VIETNAM." Humanities & Social Sciences Reviews 7, no. 2 (March 5, 2019): 100–105. http://dx.doi.org/10.18510/hssr.2019.7211.

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Purpose of this study: This study aims to examine the relationship between energy consumption, gross domestic product, and population for the period of 1985-2015. Methodology: The research questions for this study are as follows: (1) What is the association among energy consumption, GDP, population, and oil price? (2) Which suggestions can be provided on the basis of the research findings? Unit root test, co-integration test, VECM model, and Granger causality are employed to analyze the association among the aforementioned variables. Main Findings: Firstly, the results show the existence of co-integration among the variables. By employing the Granger causality, the research findings demonstrate a unidirectional causality running from population to energy consumption, a unidirectional causality running from energy consumption to gross domestic product, and a unidirectional causality running from population to gross domestic product. Implications: With these results, it is suggested that Vietnam should promote the growth of the population and the energy policies to generate economic growth. Novelty: To the best of our knowledge, this study extends the scarce literature that provides empirical findings regarding this issue.

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Jayawardhana, Thaveesha, Sachini Anuththara, Thamasha Nimnadi, Ridhmi Karadanaarachchi, Ruwan Jayathilaka, and Kethaka Galappaththi. "Asian ageing: The relationship between the elderly population and economic growth in the Asian context." PLOS ONE 18, no. 4 (April 24, 2023): e0284895. http://dx.doi.org/10.1371/journal.pone.0284895.

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The elderly population and economic growth have been a contentious topic among researchers. Regardless of the economic growth rate, the population and its growth have a stimulating influence on economic development. This study aims to explore the relationship between the elderly population and economic growth in 15 Asian countries, based on secondary data gathered from the WDI (World Development Indicators) from 1961 to 2021. This research contributes to filling the empirical gap, capturing the Granger causality concerning the relationship between the elderly population and economic growth in the Asian context in a single study. The empirical findings highlighted a one-way Granger causality from economic growth to the elderly population for India, Japan, Malaysia, and Singapore while vice versa for Bangladesh, China, and Pakistan. Furthermore, for Nepal, there is a two-way Granger causality, while there is no Granger causality for remaining countries. To the best of the authors’ knowledge, this study has been the first to investigate the relationship between the elderly population and economic growth for Asian nations, using a lengthy data series and a Granger causality test. The main findings will assist the governments, policymakers, and foreign investors in effective decision-making in this regard.

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Yan, Yuqing. "Knowledge Factors and Models of Requirements Change Management Process Based on Causality Analysis." Journal of Software 13, no. 7 (July 2018): 386–94. http://dx.doi.org/10.17706/jsw.13.7.386-394.

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DeMarie-Dreblow, Darlene. "Relation between Knowledge and Memory: A Reminder That Correlation Does Not Imply Causality." Child Development 62, no. 3 (June 1991): 484. http://dx.doi.org/10.2307/1131125.

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Ali, Wajid, Wanli Zuo, Ying Wang, and Rahman Ali. "Toward a Multi-Column Knowledge-Oriented Neural Network for Web Corpus Causality Mining." Applied Sciences 13, no. 5 (February 27, 2023): 3047. http://dx.doi.org/10.3390/app13053047.

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In the digital age, many sources of textual content are devoted to studying and expressing many sorts of relationships, including employer–employee, if–then, part–whole, product–producer, and cause–effect relations/causality. Mining cause–effect relations are a key topic in many NLP (natural language processing) applications, such as future event prediction, information retrieval, healthcare, scenario generation, decision making, commerce risk management, question answering, and adverse drug reaction. Many statistical and non-statistical methods have been developed in the past to address this topic. Most of them frequently used feature-driven supervised approaches and hand-crafted linguistic patterns. However, the implicit and ambiguous statement of causation prevented these methods from achieving great recall and precision. They cover a limited set of implicit causality and are difficult to extend. In this work, a novel MCKN (multi-column knowledge-oriented network) is introduced. This model includes various knowledge-oriented channels/columns (KCs), where each channel integrates prior human knowledge to capture language cues of causation. MCKN uses unique convolutional word filters (wf) generated automatically using WordNet and FrameNet. To reduce MCKN’s dimensionality, we use filter selection and clustering approaches. Our model delivers superior performance on the Alternative Lexicalization (AltLexes) dataset, proving that MCKN is a simpler and distinctive approach for informal datasets.

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Journal articles on the topic 'Knowledge and causality'

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