Relevant bibliographies by topics / Web Of Causation

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Web Of Causation'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Web Of Causation"

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Hawkins, Wesley E., David F. Duncan, and Regina Hovet. "Teaching Multi-Dimensional Views of Disease Causation: The Web of Causation in Epidemiology." Journal of Health Education 23, no. 5 (August 1992): 301–3. http://dx.doi.org/10.1080/10556699.1992.10610035.

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Tryon, Warren W., Thanos Patelis, Michael Chajewski, and Charles Lewis. "Theory construction and data analysis." Theory & Psychology 27, no. 1 (January 6, 2017): 126–34. http://dx.doi.org/10.1177/0959354316684043.

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Data analysis should be neutral relative to theory construction in order to be unbiased. Data analysis should not strongly favor one form of theory construction over others. Traditional approaches to theory construction prioritize simplicity of explanation based on parsimony using a few prominent statistically significant variables. Alternative web of causation explanations prioritize comprehensive explanations based on complexity using many small effects. This article presents argument and empirical evidence that contemporary data analytic methods are problematic for all theory construction approaches. They are especially biased against web of causation approaches to theory construction. Mathematical arguments and new empirical evidence that supports web of causation explanations are presented.
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Janssen, Annelli. "Het web-model." Algemeen Nederlands Tijdschrift voor Wijsbegeerte 111, no. 3 (October 1, 2019): 419–32. http://dx.doi.org/10.5117/antw2019.3.007.jans.

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Abstract The web-model: A new model of explanation for neuroimaging studiesWhat can neuroimaging tell us about the relation between our brain and our mind? A lot, or so I argue. But neuroscientists should update their model of explanation. Currently, many explanations are (implicitly) based on what I call the ‘mapping model’: a model of explanation which centers on mapping relations between cognition and the brain. I argue that these mappings give us very little information, and that instead, we should focus on finding causal relations. If we take a difference-making approach to causation, we can find manipulation patterns between neural and cognitive phenomena and start constructing satisfying explanations in neuroimaging studies: explanations based on what I call the web-model of explanation. This model of explanation not only contrasts with the mapping model, but is also different from Craver’s constitutive mechanistic model of explanation (2007), which takes the constitutive relation to be the main explanatory relation. Taking the difference-making idea of the importance of manipulation and control seriously, means that sometimes, causal relations are preferred over constitutive relations. If we follow the web-model of explanation, we can do justice to the central role that causation should play in neuroscientific explanations.
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Ventriglio, Antonio, Antonello Bellomo, and Dinesh Bhugra. "Web of causation and its implications for epidemiological research." International Journal of Social Psychiatry 62, no. 1 (January 12, 2016): 3–4. http://dx.doi.org/10.1177/0020764015587629.

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Hemida, Shaimaa A. E., Seham G. Ragheb, Hemat A. El Sayied, and Hala M. Mohamed. "Effect of Web Causation Epidemiological Model on Preventive Behaviors of Diabetic Females with Genitourinary Tract Infection." Evidence-Based Nursing Research 3, no. 1 (March 14, 2021): 12. http://dx.doi.org/10.47104/ebnrojs3.v3i1.185.

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Context: Genitourinary tract infection becomes a silent epidemic that devastated female life. It is closely interrelated with hyperglycemia, diabetes, and lack of awareness about personal hygiene care. All increase the susceptibility to various types of infections. Aim: To assess the effect of web causation epidemiological model on preventive behaviors of diabetic females with genitourinary tract infection. Methods: The study was conducted at a diabetic outpatient clinic in El Demerdash Hospital because it received clients from different areas and a high population rate. This study encompasses a purposive sample of 100 females. A quasi-experimental research design was selected. Data were collected using a structured interviewing questionnaire and preventive practice checklists to assess the knowledge and practices of the diabetic female. Results: 69% of diabetic females had a family history of diabetes mellitus. 39% of them had satisfactory knowledge about causes that increased blood glucose in diabetic females shift to 81% at post-intervention. 35% of diabetic females had satisfactory knowledge about the function of the reproductive system at preprogram shift to 93% at post-program implementation. 34% of diabetic females had satisfactory knowledge about entry urinary tract infection methods at preprogram shift to 81% at post-program implementation. A highly statistically significant association between the presence of urine infection and application of web causation model (causative factors) such as lack of hygiene during menstruation, lack of hygiene before and after sexual intercourse, using mechanical contraceptive methods, increase the intake of salty foods, poor glycemic control and lack of personal hygiene with the results of urine analysis for infection. Conclusions: Females exposed to the web causation epidemiological model exhibited better knowledge and preventive practices after exposure than their pre-intervention level. Apply the web-causation model at high population areas as slum areas on a large group of participants to improve awareness and practices regarding diabetes mellitus and genitourinary tract infection. Replicate the current study on a representative sample to improve the generalizability.
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Pechsiri, Chaveevan, and Titirut Mekbunditkul. "Extraction of cause-effect-concept pair series from web documents." Indonesian Journal of Electrical Engineering and Computer Science 18, no. 2 (May 1, 2020): 970. http://dx.doi.org/10.11591/ijeecs.v18.i2.pp970-978.

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<span>This research aims to extract a cause-effect-concept pair series of consequent event occurrences in health information of hospital web-boards. The extracted cause-effect-concept pair series representing a disease causation pathway benefits for the automatic diagnosis and solving system. Where each causative/effect event concept is expressed by an elementary discourse unit (EDU which is a simple sentence). The research has three problems; how to determine causative/effect concept EDUs from the documents containing some EDU occurrences with both causative concepts and effect concepts, how to determine the cause-effect relation between two adjacent EDUs having the discourse cue ambiguity, and how to extract cause-effect-concept pair series mingled with either a stimulation relation EDU or other non-cause-effect relation EDUs from the documents. Therefore, we apply annotated NWordCo pairs with causative-effect concepts to represent EDU pairs with causative-effect concept where the NWordCo size solved by Naïve Bayes. We also apply Naïve Bayes to solve NWordCo-concept pairs having the cause-effect relation from the adjacent EDU pairs. We then propose using cue words and the collected NWordCo-concept pairs with the cause-effect relation to extract the cause-effect-concept pair series. The research results provide the high precision of the cause-effect-concept pair series determination from the documents. </span>
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Krieger, Nancy. "Epidemiology and the web of causation: Has anyone seen the spider?" Social Science & Medicine 39, no. 7 (October 1994): 887–903. http://dx.doi.org/10.1016/0277-9536(94)90202-x.

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Denisov, E. I., L. V. Prokopenko, and S. V. Stepanyan. "OCCUPATIONAL RISK MANAGEMENT: PROGNOSIS, CAUSATION AND BIOINFORMATIONAL TECHNOLOGIES." Annals of the Russian academy of medical sciences 67, no. 6 (June 23, 2012): 51–56. http://dx.doi.org/10.15690/vramn.v67i6.284.

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Methodology of occupational risk management is outlined based on workers’ health disorders forecast and causation (work-relatedness assessment). It originates from Labour Code of Russian Federation prescriptions and includes principles, methods and criteria of risk management and risk communication. The methodology is realized by means of bioinformational technologies as expert and analytical system in the form of interactive Web-based directory «Occupational risk assessment» for practical use for occupational risk prevention.
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Kaur, Parminder, Chakshu Chaudhry, Harsha Neelam, and Inusha Panigrahi. "Bardet-Biedl syndrome presenting with laryngeal web and bifid epiglottis." BMJ Case Reports 14, no. 1 (January 2021): e236325. http://dx.doi.org/10.1136/bcr-2020-236325.

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Bardet-Biedl syndrome (BBS) is a rare autosomal recessive ciliopathy characterised by rod-cone dystrophy, obesity, postaxial polydactyly, cognitive impairment, hypogonadism, renal abnormalities, and rarely, laryngeal webs or bifid epiglottis. Most patients present with obesity. Multiple genes are involved in causation of BBS and there is also evidence of triallelic inheritance. We herein report an Asian boy who had weak cry and stridor since birth, and on evaluation was found to have both laryngeal web and bifid epiglottis. Mutation analysis revealed a homozygous variant in BBS10 gene.
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Șerban, Costela Lăcrimioara, Denis Mihai Șerban, Ștefania Ioana Butica, and Diana Lungeanu. "Web of Causation between Dietary Patterns and Childhood Obesity: Applying Hill's Criteria." Romanian Journal of Diabetes Nutrition and Metabolic Diseases 25, no. 4 (December 1, 2018): 431–38. http://dx.doi.org/10.2478/rjdnmd-2018-0052.

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Abstract Since their publication in 1965, the Bradford Hill criteria for causality have been largely used as a framework for causal inference in epidemiology. We aim at employing this classical approach to shed new light onto the web of causation of childhood obesity. Although the fundamental cause of obesity is the long-term imbalance between energetic need and intake, this medical condition is multifactorial in its origin, influenced by genetic, behavioral, socioeconomic, and environmental factors. This imbalance leads to accumulation of excessive adipose tissue. Observational studies tend to mostly quantify association between dietary factors and accumulation of adipose tissue. On the other hand, multivariate analysis proved some of these associations to be spurious, therefore prospective trials are needed to demonstrate causality. Short term experimental studies have been conducted to identify unique dietary pattern changes on specific outcomes, but long term, community-based studies would offer more comprehensive answers on dietary pattern effects. We conducted a literature review on PubMed, Scopus, Web of Science, and Google Scholar. From a total of 323 papers identified at first stage, we further discuss the applicability of Bradford Hill criteria for 31 articles, by examples of dietary patterns and accumulation of excess body fat as exposure-response associations. We also put forward and analyzed the evidence prospective studies would bring, as foundation for future interventions.
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Dissertations / Theses on the topic "Web Of Causation"

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Rust, Tom C. "Poverty and disability : caught in a web of cumulative causation /." abstract and full text PDF (free order & download UNR users only), 2006. http://0-gateway.proquest.com.innopac.library.unr.edu/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1440931.

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Thesis (M.S.)--University of Nevada, Reno, 2006.
"November 9, 2006." Includes bibliographical references (leaves 155-164). Online version available on the World Wide Web. Library also has microfilm. Ann Arbor, Mich. : ProQuest Information and Learning Company, [2006]. 1 microfilm reel ; 35 mm.
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Peleteiro, Bárbara Neves. "The role of Helicobacter pylori infection in the web of gastric cancer causation." Tese, Faculdade de Medicina da Universidade do Porto, 2010. http://hdl.handle.net/10216/62173.

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Peleteiro, Bárbara Neves. "The role of Helicobacter pylori infection in the web of gastric cancer causation." Doctoral thesis, Faculdade de Medicina da Universidade do Porto, 2010. http://hdl.handle.net/10216/62173.

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Siegel, Jeanne Hinton. "Predictors of Overweight in Children in Grades Six Through Eight." Scholarly Repository, 2007. http://scholarlyrepository.miami.edu/oa_dissertations/10.

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The rate of overweight in children is increasing at an alarming rate. The IOM (2005) estimated 9 million children over the age of six in the United States are obese. Between 1980 and 2002 the CDC (2002) estimated the rate of childhood obesity has doubled for adolescents ages 12 to 19 years (7% to 16%), and tripled for those children ages six to 11 years (5% to 16%). The health consequences of being overweight are severe and lead to decreased longevity and quality of life. The purpose of this study was to determine which factors (diet, physical activity, stress, sleep, gender, ethnicity, parental obesity, self-perception, and SES) have predictive value in the development of overweight in children in grades six through eight. The epidemiological framework, Web of Causation was used to guide this study. This model originally described by MacMahon, Pugh, and Ispen (1960) allows for the investigation of multiple causative and associated factors including lifestyle, environment, psychosocial factors, health care availability, nutrition, and physical activity. A cross-sectional predictive study was completed with 75 parent and child participants from a parochial school in south Florida. A univariate analysis of all potential predictors identified in the literature using a significance of p < .25 was performed. The dependent factor was the child's BMI greater than 85% for age and gender. Fourteen factors were included in the final forward stepwise logistic regression analysis. Instruments included family demographics, the parent and student Middle School Physical Activity and Nutrition Survey (MSPAN), the Perceived Stress Scale (PSS) and Harter's Self-Perception Scale for Children (SPSC). The sample demographics were Hispanic (60%), Caucasian (25%), and Multiethnic (8%), and other (7%). The final logistics regression model found that father's obesity (OR 5.99; p= 0.001) and Self-perception of Physical Appearance (OR 0.43; p=0.038) were predictive factors of overweight in this sample of children. The findings of this study supported that family dynamics play a part in the development of this chronic disease. Future research should be directed at defining factors that place children at risk for overweight in order to develop meaningful interventions to curb this pandemic.
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Harvey, John Steven. "Metachromatic leukodystrophy : the role of non-pathogenic sequence variants in the causation of disease /." Title page, contents and abstract only, 1996. http://web4.library.adelaide.edu.au/theses/09PH/09phh341.pdf.

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Crott, Jimmy. "The effects of folic acid deficiency and defects in folate metabolism on chromosome damage in vitro." Title page, table of contents and abstract only, 2001. http://web4.library.adelaide.edu.au/theses/09PH/09phc9515.pdf.

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Reprints of the author's previously published articles included as an appendix. Bibliography: leaves 165-188. "This thesis describes a series of experiments that aimed to investigate the effects of folic acid deficiency and defects in folate metabolism on chromosome damage rates in human lymphocytes. The accumulation of chromosome damage over time is an important issue because it is thought to contribute to the mechanism of ageing and the aetiology of diseases of age such as cancer and Alzheimer's disease."
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Carman, Judith Anne. "The metabolic relationship between nutrition and cancer /." Title page, contents and abstract only, 1988. http://web4.library.adelaide.edu.au/theses/09PH/09phc287.pdf.

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Books on the topic "Web Of Causation"

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Yin guo guan nian yu Xiumo wen ti. Beijing: Zhongguo ren min da xue chu ban she, 2010.

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Liu, Shude. Kong bai zui zhuang: Jie ding, zhui wen, jie du. Beijing: Ren min fa yuan chu ban she, 2002.

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Wer ist schuld?: Das Problem der Kausalität in Psychiatrie und Psychoanalyse : eine Untersuchung zu Martin Heideggers Zollikoner Seminaren. Hildesheim: G. Olms, 1993.

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Yuan yin zi you xing wei yan jiu: Yi zui jiu de ren fan zui wei zhong xin. Beijing Shi: Fa lü chu ban she, 2014.

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Bu chun zheng bu zuo wei fan yan jiu: Buchunzheng buzuoweifan yanjiu. Beijing: Ren min chu ban she, 2008.

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Han yu zhong jie yu yu fa wen ti yan jiu. Beijing: Shang wu yin shu guan, 2008.

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Qinming, Wang, ed. Cong "Shang han lun" kan Zhong yi bian zheng luo ji de si wei fang fa. Beijing: Zhong yi gu ji chu ban she, 2013.

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Laureno, Robert. Causation. Edited by Robert Laureno. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190607166.003.0011.

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This chapter on “Causation” examines the determination of the causes of neurologic disease. Considered are Koch-Henle postuates and Bradford Hill criteria. When we talk about “cause,” we make a distinction between necessary and sufficient causes, as well as those causes of disease that are neither necessary nor sufficient, that contribute to the development of a disease but cannot by themselves cause the disease. Probabilistic causes show their effects in combination with other probabilistic causes, known and unknown. In the absence of experimental evidence for cause, we rely on observational information. Observational study may be prospective or retrospective (case-control study). The criteria for medicolegal causation in the courtroom and in the clinic differ, and the neurologist asked to determine cause in a court of law must rely on experience, good judgment, and common sense.
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Anjum, Rani Lill, and Stephen Mumford. Evidence of Causation Is Not Causation. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198733669.003.0003.

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Philosophers draw a distinction between ontology and epistemology: a distinction between what is and what we know. What counts as evidence of causation will be fixed by our choice of methods of discovery, and our choice of methods will be fixed by what we take causation to be. Nevertheless, causation cannot be identified with its methods of discovery. Hume argued that causation was not directly observable. If this is the case, we need methods that can reliably latch on to the signs of causation. But we cannot automatically judge there to be no causation if we find no sign of it.
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Hitchcock, Christopher. Actual Causation. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198746911.003.0007.

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This chapter connects two themes in the work of Peter Menzies: (1) the agency theory of causation; and (2) the analysis of actual causation in terms of structural equation models together with considerations of normality. According to the latter type of analysis, actual causation involves certain kinds of path-specific effects. What is the practical benefit of knowing about such effects? The chapter argues that such knowledge is not necessary for one-shot decisions, but is crucial for plans that involve multiple steps. Such plans require that we know how our interventions will work in conjunction with future interventions that are feasible, expected, and desirable. This explains both the focus on path-specific effects, and the sensitivity of actual causation to considerations of normality.
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Book chapters on the topic "Web Of Causation"

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Lehmann, Jos, Joost Breuker, and Bob Brouwer. "CAUSATI O NT : Modeling Causation in AI&Law." In Law and the Semantic Web, 77–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-32253-5_6.

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Warburton, Gary. "Internal Derangements of the Temporomandibular Joint." In Oral and Maxillofacial Surgery for the Clinician, 1361–80. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-1346-6_63.

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AbstractTemporomandibular joint (TMJ) disorder (TMD) is a broad term encompassing many diseases affecting the TMJ and the surrounding structures and includes internal derangement or disc displacement disorders. Our understanding of internal derangement, its causation, and treatments have evolved over the years, and we are now able to offer effective nonsurgical and surgical management strategies. This chapter will discuss the evolution of our understanding of TMJ internal derangement, diagnosis, causation, and management strategies.
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Witting, Christian. "7. Causation and remoteness." In Street on Torts, 163–96. Oxford University Press, 2021. http://dx.doi.org/10.1093/he/9780198865506.003.0007.

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This chapter examines the issues of causation and remoteness in negligence, which basically concern the links between breaches of duty and the consequences of those breaches and the strength of those links. The chapter considers in detail causation in fact, causation in law, and remoteness of damage. We find that courts have developed several important exceptions to the ordinary ‘but for’ test of factual causation, including the Fairchild principle. Fairchild can be considered as a departure from the normal requirement that the claimant must prove factual causation of damage. Legal causation is tested by looking for unexpected events called novi actus intervenientes. Remoteness is an issue of foreseeability of damage.
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Selikowitz, Mark. "Theories of causation." In Dyslexia and Other Learning Difficulties. Oxford University Press, 1993. http://dx.doi.org/10.1093/oso/9780192622990.003.0008.

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In the definition of specific learning difficulties in the first chapter I emphasized that the delay in learning must be ‘unexplained’. It is, therefore, explicit in the definition that the cause of specific learning difficulties is presently unknown. There are few things more frustrating for a doctor to say, or for a parent to hear, than that the cause of a child’s condition is unknown. There is a natural tendency in such situations to alleviate this discomfort by guessing the cause. This is not necessarily bad, as it is by developing theories and devising experiments to test them that our knowledge advances. But the danger is that in our desire to know the cause with certainty, we may come to believe in a theory so strongly that we think of it as a fact. Theories about specific learning difficulties abound. Most are based on the assumption that there is some impairment of brain function. These theories are not mutually exclusive, since each may explain one step in the chain of events that gives rise to specific learning difficulties, as shown in Figure 3.1. Let us look at these theories one by one. These theories attempt to explain the most fundamental aspect of the condition: its primary cause. It is unlikely that a single factor can be responsible for a specific learning difficulty. Rather, it seems that a number of factors must act together. Such causation is known as ‘multifactorial’. There have been two groups of factors that have been suggested in the causation of specific learning difficulties: genetic factors and environmental factors. There is strong evidence for a genetic factor playing a role in the causation of specific learning difficulties. A number of studies have shown that children with specific learning difficulties are more likely to have a close relative with the same specific learning difficulty. No consistent pattern of inheritance has been described: sometimes it seems to be inherited from the mother, at other times from the father. For all types of such learning difficulty, boys outnumber girls by about three to one.
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Stewart, Robert. "Inference 2: causation." In Practical Psychiatric Epidemiology, 239–54. Oxford University Press, 2003. http://dx.doi.org/10.1093/med/9780198515517.003.0013.

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The study of cause and effect forms the basis for most human interaction. The repetitive investigation of actions and their consequences can be readily seen in children's behaviour. Adult behaviour may be more complex but essentially involves identical principles. When we speak to someone for the first time, an initial impression is formed. If the conversation proceeds, the impression (hypothesis) is tested and refined through evaluating actions (what we say) and their consequences (the reaction or reply this provokes). If an unknown factor is present (e.g. the other person is preoccupied with something else), the relationship between cause and effect may be misinterpreted resulting in a false impression (e.g. that they are rude or unfriendly). The process can be seen as a repeated series of experiments, albeit unconscious. All of us are therefore involved in active cause–effect research for most of our waking lives. However the inferences (whether true or false) derived from these day-to-day experiments apply only to ourselves. Science and philosophy on the other hand seek to uncover truths that are generalizable beyond the individual. Because of this, their experiments require greater scrutiny. Research may be divided into that which is observational (describing what is there) and that which is analytic (explaining why it is there). Deducing cause and effect relationships is central to analytic research. The ‘result’ of any given experiment is indisputable. What is open to interpretation is what caused that result. As discussed in Chapter 12, a series of questions have to be asked. What is the likelihood of it having occurred by chance? Was it caused by problems in the design of the study (bias), by the influence of a different factor to that hypothesised (confounding), or by a cause–effect relationship in the opposite direction to that anticipated (‘reverse’ causality)? If the anticipated cause–effect relationship is supported, what precise cause and effect were being measured in the study under consideration and how might other factors contribute to this? And what are the implications of the findings? The focus for critiquing a research report (apart from allegations of deliberate falsification) strictly speaking should not be the reported ‘Results’ but the ‘Discussion and Conclusions’—the inferences (particularly regarding cause and effect) which can be drawn from the results and therefore the generalisability of findings beyond the experimental situation.
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Wilson, Alastair. "Classifying Dependencies." In The Foundation of Reality, 46–68. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198831501.003.0003.

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Do causes always precede their effects? Is causation across a temporal gap possible? Is simultaneous causation possible? The comparative neglect of such questions means that we still lack a clear view of the underlying nature of causation. Metaphysicians typically distinguish sharply between grounding and causation, and philosophers of science typically distinguish sharply between causal and non-causal explanation, but there has been surprisingly little discussion of how exactly to draw these distinctions. This chapter argues that six of the most obvious criteria fail to capture the intended distinction between causation and grounding. The chapter proposes and defends an alternative criterion in terms of the principles mediating the dependency, and explores some of the implications of this criterion for the possibility of simultaneous causation in physics.
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Noordhof, Paul. "The Analysis of Causation." In A Variety of Causes, 1–40. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780199251469.003.0001.

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Causation is a dependency relation that need not be general. Consequently, we should focus on counterfactuals rather than regularities and laws for analysing causation. The plausibility of indeterministic cases of causation indicate that there should be appeal to probabilities, which is more successfully developed by appeal to counterfactuals rather than conditional probabilities. The analysis focuses on the nature of causation rather than our concept of it and will have reductive aims. The analysis draws upon everyday knowledge of causation in our lives and so should not be characterized as developed a priori and is not threatened by issues raised about analysis by experimental philosophers.
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Elliott, Andrew C. A. "Clarity from Chaos." In What are the Chances of That?, 265–80. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198869023.003.0015.

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Experimentalists interpret their observations as a combination of effects arising from their models of understanding and residual errors. Successive refinements of theory can reduce the observed discrepancies in a systematic way. All experimental findings are provisional, and the associated uncertainty can be quantified. Correlation does not imply causation, but causation is what we really seek: how can it be established? The process of establishing cigarette smoking as a leading cause of lung cancer is taken as a case study. General approaches to understanding causation are described.
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Noordhof, Paul. "Humean Supervenience and Possible Worlds." In A Variety of Causes, 492–528. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780199251469.003.0016.

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The position defended is compatible with a coherent development of a reductive account of modality. It is less significant for the principle of recombination than previously thought and the principle of recombination is unlikely to play successfully its role as a principle of plenitude in any event. The distinct existences principle is defended against an argument that it is necessarily false. In fact, by comparison, we have more reason to believe in the possibility of worlds in which Humean supervenience is true and there is causation, than in physicalism about phenomenal consciousness. The counterfactual theory of causation, and surrounding framework, explains why this is the appropriate verdict at which to arrive. Some aspects of the variety of causation may be understood as a determinate-determinable relation but the different vertically fundamental bases are better understood as partial realizations. Causation is one horizontally fundamental metaphysical category but there may be others.
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Schaffner, Kenneth F. "Causing Harm: Epidemiological and Physiological Concepts of Causation." In Acceptable Evidence. Oxford University Press, 1994. http://dx.doi.org/10.1093/oso/9780195089295.003.0017.

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In this chapter I shall examine the relations between what appear to be two somewhat different concepts of causation that are widely employed in the biomedical sciences. The first type is what I term epidemiological causation. It is characteristically statistical and uses expressions like "increased risk" and "risk factor." The second concept is more like the form of causation we find in both the physical sciences and everyday life, as in expressions such as "the increase in temperature caused the mercury in the thermometer to expand" or "the sonic boom caused my window to break." In the physical and the biological sciences, such claims are typically further analyzed and explained in terms of underlying mechanisms. For example, accounts in the medical literature of cardiovascular diseases associated with the ischemic myocardium typically distinguish between the risk factors and the mechanisms for these disorders (Willerson 1982). Interestingly, both concepts of causation have found their way into the legal arena, the first or epidemiological concept only relatively recently in both case law and federal agency regulatory restrictions. The second, perhaps more typical, notion of causation has turned out to be not so simple on deeper analysis and led Hart and Honoré, among others, to subject the notion to extensive study in their classic book Causation in the Law. In another paper (Schaffner 1987), I examined some of these issues, in particular the epidemiological concept of causation as it might apply to recent DES cases such as Sinddl and Collins. Reflections on the Sindell case and on one of its legal precedents, the Summers v. Tice case, led Judith Jarvis Thomson to introduce a distinction between two types of evidence that might be adduced to support a claim that an agent caused harm to a person. The two types of evidence parallel the distinction between these two concepts of causation, and 1 shall introduce them by means of a particularly striking example originally credited to David Kaye (Kaye 1982).
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Conference papers on the topic "Web Of Causation"

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Kim, Jaehyun, and David Wallace. "A Statistical Approach to Causality Analysis in a Distributed Design Framework." In ASME 2004 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2004. http://dx.doi.org/10.1115/detc2004-57694.

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Numerous collaborative design tools have been developed to accelerate the product development, and recently environments for building distributed simulations have been proposed. For example, a simulation framework called DOME (Distributed Object-oriented Modeling and Evaluation) has been developed in MIT CADLAB. DOME is unique in its decentralized structure that allows heterogeneous simulations to be stitched together while allowing proprietary information an simulation models to remain secure with each participant. While such an approach offers many advantages, it also hides causality and sensitivity information, making it difficult for designers to understand problem structure and verify solutions. The purpose of this research is to analyze the relationships between design parameters (causality) and the strength of the relationships (sensitivity) in decentralized web-based design simulation. Algorithms and implementations for the causality and sensitivity analysis are introduced. Causality is determined using Granger’s definition of causality, which is to distinguish causation from association using conditional variance of the suspected output variable. Sensitivity is estimated by linear regression analysis and a perturbation method, which transfers the problem into a frequency domain by generating periodic perturbations. Varying Internet latency and disturbances are problematic issues with these methods. Thus, new algorithms are developed and tested to overcome these problems.
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Luck, Blain R., Gregory N. Vigilante, Christopher L. Ethier, Edward J. Troiano, Brian Marquis, Hailing Yu, and David Y. Jeong. "Railroad Rails Containing Electrode-Induced Pitting From Pressure Electric Welding." In 2018 Joint Rail Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/jrc2018-6141.

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The Federal Railroad Administration (FRA) routinely conducts investigations of railroad accidents to determine causation and any contributing factors to help the railroad industry implement corrective measures that may prevent similar incidents in the future. Over the past decade, FRA has investigated multiple broken rail accidents in which fractures in the rail web were identified. The common features observed in the recovered rail fragments from these accidents included welds and spots or burn marks on the web, indicating that the rails were joined together by pressure electric welding. Pressure electric welding uses a welding head that clamps around two opposing rail ends, pressing an electrode on each rail, then hydraulically pulling the rail ends together while arcing current through the electrodes into the rails, causing them to essentially melt together to form a continuous rail. Based on the similarities observed in the web fractures, FRA rail integrity specialists hypothesized that stray (i.e. inadvertent and unwanted) arcing during pressure electric welding can result in the formation of burns or pits on the rail where it makes contact with the electrodes. Moreover, these electrode-induced pits behave as stress raisers (also referred to as stress concentrations). Fatigue cracks often develop at locations of stress concentration. Once a fatigue crack initiates, the localized stress encourages the growth of the crack, which may potentially lead to rail failure. This paper describes the forensic evaluations of three railroad rails containing electrode-induced pitting. These evaluations include: magnetic particle inspection to nondestructively detect cracks emanating from the pitting; fractography to study the fracture surfaces of the cracks; metallography to study the microstructure; analysis of chemical composition; and measurements of tensile mechanical properties and fracture toughness of rail steel. Moreover, the results of these evaluations confirm the hypothesis postulated by FRA that stray arcing during pressure electric welding can cause electrode-induced pitting.
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Bochman, Alexander. "Actual Causality in a Logical Setting." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/239.

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We provide a definition of actual causation in the logical framework of the causal calculus, which is based on a causal version of the well-known NESS (or INUS) condition. We compare our definition with other, mainly counterfactual, approaches on standard examples. On the way, we explore general capabilities of the logical representation for structural equation models of causation and beyond.
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McGowan, Joseph C., Jacob L. Fisher, and Scott R. Lucas. "Biomechanical Analysis of Occupant Kinematics: Interpretation of Witness Marks." In ASME 2008 Summer Bioengineering Conference. American Society of Mechanical Engineers, 2008. http://dx.doi.org/10.1115/sbc2008-192654.

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Understanding the occupant kinematics associated with an automotive accident is essential to evaluating injury causation and can lead to improved design of vehicles and restraint systems. Biomechanical analysis can be undertaken with knowledge of the accident reconstruction, that is, velocities and trajectories of the involved vehicle or vehicles, as well as the results of a detailed vehicle inspection and evaluation of other physical and photographic evidence. Evidence can be incomplete, seemingly contradictory, or compromised by the passage of time, and the biomechanical engineer must seek an explanation that is consistent with all that is known. Sometimes a physical examination of the accident vehicle provides a vivid understanding of injury causation. At other times the information obtained from the vehicle is understood only in the context of injuries sustained, witness statements, and/or information derived from other sources. We explain a methodology for conduct of a physical inspection of an accident vehicle to develop insight to be used in conjunction with information from other sources to elicit a clear and complete understanding of injury causation. We specify common and less common “witness marks” that are examined to develop constraints on possible occupant kinematics. Selected case studies highlight the importance of a careful inspection and suggest specific applications to accident scenarios.
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Peyrard, Maxime, and Robert West. "A Ladder of Causal Distances." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/277.

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Causal discovery, the task of automatically constructing a causal model from data, is of major significance across the sciences. Evaluating the performance of causal discovery algorithms should ideally involve comparing the inferred models to ground-truth models available for benchmark datasets, which in turn requires a notion of distance between causal models. While such distances have been proposed previously, they are limited by focusing on graphical properties of the causal models being compared. Here, we overcome this limitation by defining distances derived from the causal distributions induced by the models, rather than exclusively from their graphical structure. Pearl and Mackenzie [2018] have arranged the properties of causal models in a hierarchy called the ``ladder of causation'' spanning three rungs: observational, interventional, and counterfactual. Following this organization, we introduce a hierarchy of three distances, one for each rung of the ladder. Our definitions are intuitively appealing as well as efficient to compute approximately. We put our causal distances to use by benchmarking standard causal discovery systems on both synthetic and real-world datasets for which ground-truth causal models are available.
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Zeng, Shuai, Maria Skrabisova, Zhen Lyu, Yen On Chan, Kristin Bilyeu, and Trupti Joshi. "SNPViz v2.0: A web-based tool for enhanced haplotype analysis using large scale resequencing datasets and discovery of phenotypes causative gene using allelic variations." In 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2020. http://dx.doi.org/10.1109/bibm49941.2020.9313539.

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Zügner, Daniel, Amir Akbarnejad, and Stephan Günnemann. "Adversarial Attacks on Neural Networks for Graph Data." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/872.

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Deep learning models for graphs have achieved strong performance for the task of node classification. Despite their proliferation, currently there is no study of their robustness to adversarial attacks. Yet, in domains where they are likely to be used, e.g. the web, adversaries are common. Can deep learning models for graphs be easily fooled? In this extended abstract we summarize the key findings and contributions of our work, in which we introduce the first study of adversarial attacks on attributed graphs, specifically focusing on models exploiting ideas of graph convolutions. In addition to attacks at test time, we tackle the more challenging class of poisoning/causative attacks, which focus on the training phase of a machine learning model. We generate adversarial perturbations targeting the node's features and the graph structure, thus, taking the dependencies between instances in account. Moreover, we ensure that the perturbations remain unnoticeable by preserving important data characteristics. To cope with the underlying discrete domain we propose an efficient algorithm Nettack exploiting incremental computations. Our experimental study shows that accuracy of node classification significantly drops even when performing only few perturbations. Even more, our attacks are transferable: the learned attacks generalize to other state-of-the-art node classification models and unsupervised approaches, and likewise are successful given only limited knowledge about the graph.
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Amaral , Thábata, and Eliane Souza. "TRiER: A Fast and Scalable Method for Mining Temporal Exception Rules." In XXXIV Simpósio Brasileiro de Banco de Dados. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/sbbd.2019.8803.

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Association rules are a common task to discover useful and comprehensive relationships among items. Our interest is to find exception rules, i.e. patterns that rarely occur but have critical consequences. Existing approaches for exception rules usually handle Itemset databases and are unfeasible for mining large ones due to high computational complexity. We thus propose TRiER (TempoRal Exception Ruler), an efficient method for mining temporal exception rules that not only discover unusual behaviors and their causative agents, but also identifies how long consequences take to appear. We performed an extensive experimental analysis in real data and results show TRiER is faster and more scalable than existing approaches while finding meaningful rules.
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Musicki, Zoran, and Ted Ginsberg. "Comparison of Novovoronezh Unit 5 NPP and South Ukraine Unit 1 NPP Level 1 PRA Results." In 10th International Conference on Nuclear Engineering. ASMEDC, 2002. http://dx.doi.org/10.1115/icone10-22601.

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This paper describes a study undertaken to explain the risk profile differences in the results of PRAs of two similar VVER-1000 nuclear power plants. The risk profile differences are particularly significant in the area of small steam/feedwater line breaks, small-small LOCAs, support system initiators and containment bypass initiators. A top level (limited depth) approach was used in which we studied design differences, major assumptions, data differences, and also compared the two PRA analyses on an element-by-element basis in order to discern the major causative factors for the risk profile differences. We conclude that the major risk profile differences are due to differences in assumptions and engineering judgment (possibly combined with some design and data differences) involved in treatment of uncertain physical phenomena (primarily sump plugging in LOCAs and turbine building steaming effects in secondary system breaks). Additional major differences are attributable to support system characteristics.
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10

Jain, Swati, Francois Ayello, John A. Beavers, and Narasi Sridhar. "Development of a Probabilistic Model for Stress Corrosion Cracking of Underground Pipelines Using Bayesian Networks: A Concept." In 2012 9th International Pipeline Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/ipc2012-90340.

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Stress corrosion cracking (SCC) continues to be a safety concern, mainly because it can remain undetected before a major pipeline failure occurs. SCC processes involve complex interactions between metallurgy, stress, external soil environment, and the electrolyte chemistry beneath disbonded coatings. For these reasons, assessing SCC failure probability at any given location on a pipeline is difficult. In an attempt to assess the SCC probability, a Bayesian network model was created. The model links events by cause-consequence connections. The strengths of these connections are adjusted using expert knowledge, analytical models, and data from the field. Bayesian network modeling was chosen because it takes into account the high degree of uncertainty in the input parameters. Other models have been developed to assess SCC: such as indexing methods, heuristics models, and mechanistic models. However, their main limitation is the uncertainty of the input parameters. One other strength of the Bayesian model is that calculations can be run in two directions: the forward direction from cause to consequence and the backward direction from observation to causative factors. In the forward direction, the model evaluates the probability of SCC failure using various input probabilities of factors that are important to SCC. In the backward direction, the model can evaluate the effect of any known occurrence of SCC failure on the probabilities of causative factors and thus condition the Bayesian network to evaluate the future failure probability. In this paper, we discuss a Bayesian network model for high-pH SCC. The conceptual framework, acquisition of data, and the inclusion of uncertainties are described. In addition, an example of the model application to high pH SCC is given. The effects of service and field conditions such as soil type, soil chemistry, coating type, surface preparation techniques, stresses, residual stress due to pipe manufacturing conditions, welds, dents, location such as proximity to rivers, wetting and drying cycles, etc. on the SCC probability can be assessed with the model. The model details shown in this publication will only cover the stress affect due to surface preparation, welds, dents, and manufacturing conditions and temperature effect. The effects of other factors and validation against field experience will be discussed in future publications.
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