Concept Of Causality Research Articles

The perceived dilution of causal strength

Dependency theories of causal reasoning, such as causal Bayes net accounts, postulate that the strengths of individual causal links are independent of the causal structure in which they are embedded; they are inferred from dependency information, such as statistical regularities. We propose a psychological account that postulates that reasoners’ concept of causality is richer. It predicts a systematic influence of causal structure knowledge on causal strength intuitions. Our view incorporates the notion held by dispositional theories that causes produce effects in virtue of an underlying causal capacity. Going beyond existing normative dispositional theories, however, we argue that reasoners’ concept of causality involves the idea that continuous causes spread their capacity across their different causal pathways, analogous to fluids running through pipe systems. Such a representation leads to the prediction of a structure-dependent dilution of causal strength: the more links are served by a cause, the weaker individual links are expected to be. A series of experiments corroborate the theory. For continuous causes with continuous effects, but not in causal structures with genuinely binary variables that can only be present or absent, reasoners tend to think that link strength decreases with the number of links served by a cause. The effect reflects a default notion reasoners have about causality, but it is moderated by assumptions about the amount of causal capacity causes are assumed to possess, and by mechanism knowledge about how a cause generates its effect(s). We discuss the theoretical and empirical implications of our findings.

Some Epistemological and Ontological Reflections on Concept of Causality: From Scientific Causality to Contextual Causality

This research tries to present a critical analysis of the concept of scientific causality and the problems it entails within the scientific domain. This study claims that scientific causality is not enough to understand the nature of relationships existing among the members of a chain and it is concerned with the possible action of correspondence apart from the context of action. It is argued here that all types of scientific causality fail to reflect the flexible nature of members of any possible correlation. This paper offers the concept "contextual causality" as a possible alternative perspective that reflects both the variable and elastic nature of the elements of correlation and discusses the relationship between the context and the process of correlation. The introduction of the concept of contextual causality raises new metaphysical and epistemological issues that can be solved from the perspective of structural idealism. 
 Keyword: Causality, Contextualism, Structuralism, Structural Idealism, Linear Causality, Epistemology.

Perpetual Peace: An Analysis of Kant’s Theory of Peace in Terms of The Islamic Worldview

This study critically discussed the concept of 'perpetual peace' according to Immanuel Kant. For Kant, peace is absolute, and war must be avoided. Kant's concept of peace departs from his philosophical ideas about humans and ethics. According to the researcher's hypothesis, Kant's concept of peace is problematic and not compatible with the concept of peace in Islam. This study aimed to describe Immanuel Kant's concept of perpetual peace and the concept of peace in Islam. In addition, it also analyzed how Kant's concept of perpetual peace is viewed from the Islamic worldview. This research was library research, and the methods used were descriptive, analytical, and comparative. This research concluded that Kant's concept of peace had several philosophical and practical problems. Philosophically, Kant's concept of peace revolves around the concept of man, ethics, and causality, which tend to deny the element of God in it. In practical terms, Kant's idea that war should not exist is a dream that is difficult to realize. His opinion about the peace triangle also leaves many problems. In contrast to that, in the view of Islam, war is allowed to fight injustice and aim for the creation of goodness or maslahah, justice, and global peace. War in Islam is also regulated based on ethical and humanitarian principles. This is based on the Islamic view of humans who simultaneously have a good and a wrong side. In addition, Islam focuses more on human nature and its perspective on reality and life to achieve peace. The Qur'an, with normative teachings such as equality between humans, justice, honesty, the nature of qanaah, can be used as a normative and practical basis for achieving perpetual peaceKeywords : Perpetual Peace; Immanuel Kant; Islamic Worldview

Від темпоральних даних до динамічних каузальних моделей

We present a brief review of dynamic causal model inference from data. A vector autoregressive models is of our prime interest. The architecture, representation and schemes of measurement of temporal data and time series data are outlined. We argue that require- ment to data characteristics should come from the nature of dynamic process at hand and goals of model inference. To describe and evaluate temporal data one may use terms of longitude, measurement frequency etc. Data measurement frequency is crucial factor in order to an inferred model be adequate. Data longitude and observation session duration may be expressed via several temporal horizons, such as closest horizon, 2-step horizon, influence attainability horizon, oscillatory horizon, and evolutionary horizon. To justify a dynamic causal model inference from data, analyst needs to assume the dynamic process is stationary or at least obeys structural regularity. The main specificity of task of dynamic causal model inference is known temporal order of variables and certain structural regularity. If maximal lag of influence is unknown, inference of dynamic causal model faces additional problems. We examine the Granger’s causality concept and outline its deficiency in real circumstances. It is argued that Granger causality is incorrect as practical tool of causal discovery. In contrast, certain rules of edge orientation (included in known constraint-based algorithms of model inference) can reveal unconfounded causal relationship.

Traditional Igbo Belief in Causes of Disease: An Evaluation

The mystical causes of diseases related with the Igbo traditional belief system are briefly investigated in this research. The specific goals are to look at mystical/spiritual reasons and techniques of fighting diseases, as well as their benefits and drawbacks in terms of disease therapy. For data analysis, the study used a qualitative phenomenological research design and a descriptive approach. Personal interviews were the major source of data collecting, whereas library materials were the secondary source. According to the study, several mystical forces in Igbo cosmology, sorcerers, Ogbanje, curses arising from the violation of taboos and oaths are to be blamed for untimely deaths and sicknesses befalling people. As a result, even in the face of western Germ theory, the Igbo belief in mystical causes of disease has proven that Germ theory, as far as the Igbo traditional conception of disease causation is concerned, does not satisfy the Igbo belief in what causes diseases, as some diseases have defied western medication. The study proposes, among other things, that hospitals in Nigeria should take into account mystical agents as well as pathogenic agents in order to provide proper and adequate treatment for the people who believe in such traditional approached.

A Methodological Framework for Investigating Diversity in Clinical Research

AbstractBackgroundMany reports and reviews have been published on best practices in research recruitment, engagement, and retention (RER), yet clinical research broadly lacks identifiable mechanisms to translate these qualitative reports into quantifiable, testable hypotheses on the science of RER. A science of diversity requires precise mechanisms for true efficacy, but before this is possible, we must develop key methodological and ontological frameworks. Thus, we are in dire need a basic sciences approach to identify mechanisms that underpin RER research, and disparities in Alzheimer’s disease and related dementias (ADRD) clinical research more broadly.MethodA science of diversity in clinical trials must ensure research conducted under the umbrella of this discipline is not methodologically confounded. Although researchers studying health inequities have begun to parse health disparitiesfrom health inequities, recruitment into research may still conflate foundational causal concepts, obscuring researchers’ ability to truly determine the nature of group differences. An ordered methodological framework was proposed, formalizing key biases that may confound efforts to diversity clinical research samples, and to determine the nature of health inequities and disparities among marginalized and minoritized populations.ResultIn addition to aspects of biological differences and structural inequities that may inform group differences in both RER and ADRD clinical research, three additional sources of bias were identified: biases of selection and inclusion into research, error and biases of measurement, and biases of implementation of research protocols. Notably, existing tools to measure and mitigate these sources of noise are available, ensuring that detected group differences are not confounded by shortcomings in scientific design.ConclusionI present this novel framework to support a rigorous methodological approach to the study of diversity in ADRD clinical trials, anchoring a formalized science of RER and ensuring that estimates of health disparities and inequities in ADRD research are free of biases introduced by well‐intentioned research teams. Implementing this methodological approach is expected to improve accrual of diverse and representative populations into ADRD research, and to improve the quality and generalizability of ADRD clinical research itself.

Al-Ghazâlî’s Concept of Causality and Quantum Physics: Finding a Point of Relevance

Al-Ghazâlî views the connection between what is believed to be natural cause and effect is not necessary. For him, God is the Agent in every event. He is the determining factor. As a result, a natural event cannot be seen with certainty, in other words it is probabilistic. On the other hand, quantum physics developed in the early 20 th century, which tries to explain the microscopic world is only able to predict events probabilistically. So it can be said that quantum physics that emerged at the beginning of the 20th century supports and affirms the views of al-Ghazâlî formulated in the 11th century at least in terms of probability. In addition, the many strange and complicated things that have not been well explained in the atomic world should be an indication that a more in-depth and thorough study of the universe leads to an increasingly "visible" Entity that is Almighty, namely God—where He has made nature as the âyât (signs) of His existence. Furthermore, from this study of al-Ghazâlî’s concept of causality and quantum physics, it can be understood that there are differences that become the end of the problem, namely metaphysical beliefs. So that an important effort that must be made to build Islamic science is to form an intellectual commitment based on metaphysical beliefs originating from Revelation.

Explaining causality of node (non-)participation in network communities

Discovering communities in networks constitutes an active and significant research field, especially in real-world networks, where communities usually represent functional units of these networks. In the introduced work, we study the community discovery problem in real-world networks from a different perspective. Our motivation stems from inquiries related to the cause of a node’s participation in a community. Therefore, we incorporate the concepts of causality and responsibility into a novel framework. Our goal is to set up a framework to discover explainable causal relations between nodes and communities, which is a topic that has not been investigated before. We evaluate the proposed framework by experimenting on real-world networks. Moreover, the introduced framework is flexible to be used to other network-related analysis tasks, beyond community detection, such as link prediction, anomaly detection, influence maximization.

SPECIFICATION OF THE SIGNIFICANCE OF THE PRINCIPLE OF DETERMINISM IN MODERN SCIENTIFIC THEORY

Determinism appears as a key concept for science from the moment of its emergence: science is designed to clearly and unambiguously determine the causes of observed phenomena and, on this basis, confidently predict the development of these phenomena. However, more than a century ago, after the emergence of quantum physics and the formulation of the theory of relativity and the principle of uncertainty, the classical understanding of determinism underwent a significant revision in physics, which was considered a model of science. Even more challenges to the concept of determinism have been received in the social sciences and humanities, which address objects that cannot be fully grasped by human experience and tested through experiments. At the same time, it is the social sciences and humanities that are increasingly being turned to for answers to the questions of what the future of society will look like and how to prepare for its challenges. All this led to the need to clarify to what extent the concept of determinism itself has retained its significance for science, as well as to clarify the modern understanding of concepts related to it, such as causality, indeterminism, uncertainty, etc. Important ideas and problems that have not been adequately understood to date were formulated in the classic works of prominent representatives of the natural sciences and humanities, in particular, such as the physicist Werner Heisenberg and the historian and sociologist Raymond Aron. With the development of science, the concept of causality only becomes more complicated and develops as a theoretical justification of the relations of causality, determinism and interdependence, and at the same time as an approximate model of causality as an objective and regular relationship of things, processes and phenomena. A separate task of science is to carry out a public clarification of essential changes in the concept of scientific determinism as an assumption about possible stable repeated connections, without which, however, scientific or even quasi-scientific systematization of knowledge is impossible.

The Historical Criticism and Causality in The Philosophy of History: Ibn Khaldun’s Perspective

This study will reveal how ibn Khaldun's philosophy of history, especially on his historical criticism and the concept of causality. This research is based on literature and qualitative research that uses a philosophical approach to explain the meaning of philosophy of history. This research presented Ibn Khaldun's criticism on historians' subjectivity in observing history. He believed that history progressed in the spiral pattern that moved repetitively, but the repetition continued towards progression. According to Ibn Khaldun, history is under the laws that control it, and the historical reality is a unity, not independent, and has a relationship with each other. To know the nature of civilization, all the needs to do is know the causes, principles, and elements contained in a civilization. There are four causes make the nature of civilization; ‘umrân as material cause, daulah as formal cause, ‘ashabiyah as efficient cause, and mashâlih ‘âmmah as final cause.

Introducing Causal Inference Using Bayesian Networks and do-Calculus

We present an instructional approach to teaching causal inference using Bayesian networks and do-Calculus, which requires less prerequisite knowledge of statistics than existing approaches and can be consistently implemented in beginner to advanced levels courses. Moreover, this approach aims to address the central question in causal inference with an emphasis on probabilistic reasoning and causal assumption. It also reveals the relevance and distinction between causal and statistical inference. Using a freeware tool, we demonstrate our approach with five examples that instructors can use to introduce students at different levels to the conception of causality, motivate them to learn more concepts for causal inference, and demonstrate practical applications of causal inference. We also provide detailed suggestions on using the five examples in the classroom.

NEXO DE CAUSALIDADE NA AVALIAÇÃO DO DANO À PESSOA

INTRODUCTION: The discussion between the knowledge of medicine and law is increasingly relevant, especially with the growing demand for medical expertise in society. The objective of this work is to address the causal link in legal medicine and medical expertise. METHODOLOGY: Critical and anlytical reflection of the bibliography with proposals that innovate thinking on the subject. Research was carried out in the Medline, Virtual Health Library, Periodicals Capes and Scielo Brasil databases, with the aim of substantiating the relevance of the topic for expert action. RESULTS/DISCUSSION: We discuss the concept of cause and its historical basis, causality and imputation. Causal link in medicine and legal medicine and medical expertise/legal medicine/forensic sciences. CONCLUSION: Causal link in legal medicine and medical expertise assesses the relationship between alleged cause and existing damage (disease, disorder or injury, with injury to the person). In cases of trauma it is based on Hregardinill’s criteria. However, in cases of diseases and disorders, the definition of the initial moment of the disease or the disorder is compromised, requiring new methods and techniques to withstand such complexity, regarding this relationship. We ask: How to enrich scientific theory and methodology beyond hypothesis testing? How to use explanatory concepts and models, capable of addressing complex issues? How to take health beyond mere risk configuration? How to be transdisciplinary, where interdisciplinary work is not yet a reality? The answers to these questions depend on the future and evolution of science that involves the establishment of link in the evaluation of personal injury.

Causation without the causal theory of action

Abstract This paper takes a critical stance on Tallis’s separation of causation and agency. While his critique of the causal theory of action and the assumptions about causation underlying different versions of determinism, including the one based on neuroscience is right, his rejection of causation (of all sorts) has implausible consequences. Denying the link between action and causation amounts to overlooking the role action plays in causal inference and in the origin of causal concepts. I suggest that a weaker version of Tallis’ claim, compatible with causation understood as agency, would work better.

CHIME: Causal Human-in-the-Loop Model Explanations

Explaining the behaviour of Artificial Intelligence models has become a necessity. Their opaqueness and fragility are not tolerable in high-stakes domains especially. Although considerable progress is being made in the field of Explainable Artificial Intelligence, scholars have demonstrated limits and flaws of existing approaches: explanations requiring further interpretation, non-standardised explanatory format, and overall fragility. In light of this fragmentation, we turn to the field of philosophy of science to understand what constitutes a good explanation, that is, a generalisation that covers both the actual outcome and, possibly multiple, counterfactual outcomes. Inspired by this, we propose CHIME: a human-in-the-loop, post-hoc approach focused on creating such explanations by establishing the causal features in the input. We first elicit people's cognitive abilities to understand what parts of the input the model might be attending to. Then, through Causal Discovery we uncover the underlying causal graph relating the different concepts. Finally, with such a structure, we compute the causal effects different concepts have towards a model's outcome. We evaluate the Fidelity, Coherence, and Accuracy of the explanations obtained with CHIME with respect to two state-of-the-art Computer Vision models trained on real-world image data sets. We found evidence that the explanations reflect the causal concepts tied to a model's prediction, both in terms of causal strength and accuracy.

Decomposing predictability to identify dominant causal drivers in complex ecosystems

Ecosystems are complex systems of various physical, biological, and chemical processes. Since ecosystem dynamics are composed of a mixture of different levels of stochasticity and nonlinearity, handling these data is a challenge for existing methods of time series-based causal inferences. Here, we show that, by harnessing contemporary machine learning approaches, the concept of Granger causality can be effectively extended to the analysis of complex ecosystem time series and bridge the gap between dynamical and statistical approaches. The central idea is to use an ensemble of fast and highly predictive artificial neural networks to select a minimal set of variables that maximizes the prediction of a given variable. It enables decomposition of the relationship among variables through quantifying the contribution of an individual variable to the overall predictive performance. We show how our approach, EcohNet, can improve interaction network inference for a mesocosm experiment and simulated ecosystems. The application of the method to a long-term lake monitoring dataset yielded interpretable results on the drivers causing cyanobacteria blooms, which is a serious threat to ecological integrity and ecosystem services. Since performance of EcohNet is enhanced by its predictive capabilities, it also provides an optimized forecasting of overall components in ecosystems. EcohNet could be used to analyze complex and hybrid multivariate time series in many scientific areas not limited to ecosystems.

Two Concepts of Cause in Antiphon’s Second Tetralogy

Abstract Using a framework from recent metaphysics and philosophy of science, according to which we have two concepts of cause, producer and necessary condition, we investigate causal notions in Antiphon’s Second Tetralogy, which concerns the unintentional homicide of a boy by a javelin-throwing youth. The prosecution maintains that the youth, having produced the boy’s death, is legally responsible; the defense argues, first, that the youth is patient, not agent, of a missing-the-target, and second, that the boy’s death depends on his running into the javelin’s path, so that identifying the cause requires evaluating the contrasts implicit in any causal claim.

The Representation of Causality and Causation with Ontologies: A Systematic Literature Review.

To explore how disease-related causality is formally represented in current ontologies and identify their potential limitations. We conducted a systematic literature search on eight databases (PubMed, Institute of Electrical and Electronic Engendering (IEEE Xplore), Association for Computing Machinery (ACM), Scopus, Web of Science databases, Ontobee, OBO Foundry, and Bioportal. We included studies published between January 1, 1970, and December 9, 2020, that formally represent the notions of causality and causation in the medical domain using ontology as a representational tool. Further inclusion criteria were publication in English and peer-reviewed journals or conference proceedings. Two authors (SS, RM) independently assessed study quality and performed content analysis using a modified validated extraction grid with pre-established categorization. The search strategy led to a total of 8,501 potentially relevant papers, of which 50 met the inclusion criteria. Only 14 out of 50 (28%) specified the nature of causation, and only 7 (14%) included clear and non-circular natural language definitions. Although several theories of causality were mentioned, none of the articles offers a widely accepted conceptualization of how causation and causality can be formally represented. No current ontology captures the wealth of available concepts of causality. This provides an opportunity for the development of a formal ontology of causation/causality.

Canadian School Administrators' Statistical Reasoning about Probability, Effect, and Representativeness

How do Canadian school leaders interpret data to inform their decisions? How do they reason with probability concepts? These are the questions we are investigating in the first year of this longitudinal bilingual project conducted in Alberta, British Columbia, and Ontario. Our theoretical framework is inspired by the semiotic perspective of Charles Sanders Peirce (1839-1914) which suggests that interpretation is a triadic process integrated in a social context that puts in relation a sign, an object, and an interpretant. To this end, we conducted two individual interviews in which we asked 10 English-speaking school leaders and 9 French-speaking school leaders some questions about data presented in a tabular form (mock data on class level student performance and school level health data), line graph (PISA 2018 report on reading scores from 2009 to 2018) and box plots (mock data on student performance in reading in different countries). Our preliminary results reveal that principal’s reason abductively when it comes to interpreting statistics and want to know the context or the story behind the numbers before making any decisions. Also, they prefer to interpreting data collaboratively with their colleagues and feel more comfortable with data grouped in tables and line graphs. They considered themselves "data-driven" but not statisticians and use verbal terms rather than ratios or percentages (e.g., high probability, high likelihood, high odds) to express probabilistic ideas. In the next years, we will study how their professional experiences influence their conceptions of causality and how they reason about sampling and representativeness.

Inferential Pluralism in Causal Reasoning from Randomized Experiments.

Causal pluralism can be defended not only in respect to causal concepts and methodological guidelines, but also at the finer-grained level of causal inference from a particular source of evidence for causation. An argument for this last variety of pluralism is made based on an analysis of causal inference from randomized experiments (RCTs). Here, the causal interpretation of a statistically significant association can be established via multiple paths of reasoning, each relying on different assumptions and providing distinct elements of information in favour of a causal interpretation.

A Hierarchical Framework for Assessing Transmission Causality of Respiratory Viruses.

Systematic reviews of 591 primary studies of the modes of transmission for SARS-CoV-2 show significant methodological shortcomings and heterogeneity in the design, conduct, testing, and reporting of SARS-CoV-2 transmission. While this is partly understandable at the outset of a pandemic, evidence rules of proof for assessing the transmission of this virus are needed for present and future pandemics of viral respiratory pathogens. We review the history of causality assessment related to microbial etiologies with a focus on respiratory viruses and suggest a hierarchy of evidence to integrate clinical, epidemiologic, molecular, and laboratory perspectives on transmission. The hierarchy, if applied to future studies, should narrow the uncertainty over the twin concepts of causality and transmission of human respiratory viruses. We attempt to address the translational gap between the current research evidence and the assessment of causality in the transmission of respiratory viruses with a focus on SARS-CoV-2. Experimentation, consistency, and independent replication of research alongside our proposed framework provide a chain of evidence that can reduce the uncertainty over the transmission of respiratory viruses and increase the level of confidence in specific modes of transmission, informing the measures that should be undertaken to prevent transmission.