Causal Theory Research Articles

Causal third-order viscous hydrodynamics within relaxation-time approximation

In the present work, we derive a linearly stable and causal theory of relativistic third-order viscous hydrodynamics from the Boltzmann equation with relaxation-time approximation. We employ viscous correction to the distribution function obtained using a Chapman-Enskog like iterative solution of the Boltzmann equation. Our derivation highlights the necessity of incorporating a new dynamical degree of freedom, specifically an irreducible tensor of rank three, within this framework. This differs from the recent formulation of causal third-order theory from the method of moments which requires two dynamical degrees of freedom: an irreducible third-rank and a fourth-rank tensor. We verify the linear stability and causality of the proposed formulation by examining perturbations around a global equilibrium state. Published by the American Physical Society 2024

Theory as time travel: Patomäki, World Statehood and possible futures

ABSTRACT In this review essay, I set explore Heikki Patomäki's seventh sole-authored book in English, World Statehood. I set out the thematic structure and chapter order and then address whether the concept of ‘self-transformative capacity of contexts’ implies a central conflation and what is assumed if one argues that there is a tendential form of civilizational progress. I conclude with discussion of a causal process theory of time.

Cracking Chicken-Egg Conundrums: Juxtaposing Contemporaneous and Lagged Reciprocal Effects Models of Academic Self-Concept and Achievement’s Directional Ordering

Multi-wave-cross-lagged-panel models (CLPMs) of directional ordering are a focus of much controversy in educational psychology and more generally. Extending traditional analyses, methodologists have recently argued for including random intercepts and lag2 effects between non-adjacent waves and giving more attention to controlling covariates. However, the related issues of appropriate time intervals between waves (lag1 intervals across waves) and the possibility of contemporaneous (lag0) effects within each wave are largely unresolved. Although philosophers, theologians, and scientists widely debate sequential (lagged) and simultaneous (lag0) theories of causality, CLPM researchers have mostly ignored contemporaneous effects, arguing causes must precede effects. In a substantive-methodological synergy, we integrated these issues and designed new structural equation models to reanalyze one of the strongest CLPM studies of academic self-concept (ASC) and achievement (five annuals of mathematics data; 3527 secondary school students). A taxonomy of models incorporating various combinations of lag0, lag1, and lag2 effects, random intercepts, and covariates consistently supported a priori reciprocal effect model (REM) predictions—medium or large reciprocal effects of ASC and achievement on each other. Consistent with self-concept theory, effects of ASC on achievement evolved over time (lag1, not lag0 effects), whereas effects of achievement on ASC effects were more contemporaneous (lag0, not lag1 effects). We argue that lag0 effects reflect proximal events occurring subsequent to the previous data wave, suggesting the need for shorter intervals but also leaving open the possibility of contemporaneous effects that are truly instantaneous. We discuss limitations and future directions but also note the broad applicability of our statistical models.

Causal self-dual electrodynamics

Many theories of nonlinear electrodynamics (NLED) that have been proposed in physical contexts involving strong fields are causal for weak fields but acausal for strong fields. We show that for any such theory there is a unique causal and self-dual (electromagnetic duality invariant) theory with the same Lagrangian at zero magnetic field. This follows from a construction of the general causal self-dual NLED, which shows that strong-field causality is implied by weak-field causality for self-dual theories. We illustrate our results with explicit examples. Published by the American Physical Society 2024

Hume on causation: against the quasi-realist interpretation

ABSTRACT In recent years, a number of philosophers have promoted a quasi-realist (or projectivist) interpretation of Hume's theory of causation. In this paper, we argue against the quasi-realist interpretation of Hume, on the grounds that there is a direct clash between a fundamental element of Hume's system (his empiricist theory of content) and one of the main constraints that governs any form of quasi-realism (and so a fortiori, quasi-realism about causation).

Comparing decision-making strategies in accounting franchises and independent brands: a causation and effectuation approach

Comparing decision-making strategies in accounting franchises and independent brands: a causation and effectuation approach

In-in correlators and scattering amplitudes on a causal set

Causal set theory is an approach to quantum gravity in which spacetime is fundamentally discrete at the Planck scale and takes the form of an irregular Lorentzian lattice, or “causal set,” from which continuum spacetime emerges in a large-scale (low-energy) approximation. In this work, we present new developments in the framework of interacting quantum field theory on causal sets. We derive a diagrammatic expansion for in-in correlators in local scalar field theories with finite polynomial interactions. We outline how these same correlators can be computed using the double-path integral, which acts as a generating functional for the in-in correlators. We modify the in-in generating functional to obtain a generating functional for in-out correlators. We define a notion of scattering amplitudes on causal sets with noninteracting past and future regions and verify that they are given by S-matrix elements (matrix elements of the time-evolution operator). We describe how these formal developments can be implemented to compute early Universe observables under the assumption that spacetime is fundamentally discrete. Published by the American Physical Society 2024

Complexity embraced: a new perspective on the evaluation of organisational interventions

ABSTRACT Organisational interventions are recommended to address the root causes of ill-health in organisations. Yet, the evidence for their effectiveness is inconclusive, likely because such interventions are complex, and their effectiveness depends on how and in which contexts the interventions are implemented. This makes organisational interventions challenging to evaluate. While multiple factors affecting implementation and intervention outcomes have been uncovered, it remains unclear which of them are necessary and which are sufficient to produce desired outcomes. To move forward, we argue that the field would benefit from using a theory of causation that better reflects that factors can combine in various ways, that there may be multiple paths to the same outcome, and that a factor can be necessary for bringing about an outcome and thus always leads to it, or sufficient, implying that multiple factors can independently lead to the same outcome. We believe that the use of evaluation designs that align with this type of causation, such as the configurational comparative methods in general and coincidence analysis in particular, will be a significant turning point for the field. The proposed paradigm will improve the precision of current frameworks and models for the evaluation and implementation of organisational interventions.

Prediction and causal inference of cardiovascular and cerebrovascular diseases based on lifestyle questionnaires

Cardiovascular and cerebrovascular diseases (CCVD) are prominent mortality causes in Japan, necessitating effective preventative measures, early diagnosis, and treatment to mitigate their impact. A diagnostic model was developed to identify patients with ischemic heart disease (IHD), stroke, or both, using specific health examination data. Lifestyle habits affecting CCVD development were analyzed using five causal inference methods. This study included 473,734 patients aged ≥ 40 years who underwent specific health examinations in Kanazawa, Japan between 2009 and 2018 to collect data on basic physical information, lifestyle habits, and laboratory parameters such as diabetes, lipid metabolism, renal function, and liver function. Four machine learning algorithms were used: Random Forest, Logistic regression, Light Gradient Boosting Machine, and eXtreme-Gradient-Boosting (XGBoost). The XGBoost model exhibited superior area under the curve (AUC), with mean values of 0.770 (± 0.003), 0.758 (± 0.003), and 0.845 (± 0.005) for stroke, IHD, and CCVD, respectively. The results of the five causal inference analyses were summarized, and lifestyle behavior changes were observed after the onset of CCVD. A causal relationship from ‘reduced mastication’ to ‘weight gain’ was found for all causal species theory methods. This prediction algorithm can screen for asymptomatic myocardial ischemia and stroke. By selecting high-risk patients suspected of having CCVD, resources can be used more efficiently for secondary testing.

“Recasting sorcery as critical psycho-social commentary, moral discourse, and local psychotherapy”

This article critically reflects on future directions in cross-cultural psychiatry and cultural psychology by engaging the challenges of interpreting psycho-social theories of causation and evidence in what is conventionally called “sorcery” in anthropology. This anthropologist argues that sorcery, notwithstanding its status as an older, classic topic in the history of anthropology and its seemingly “exotic” reputation, has continuing value in recent efforts to de-colonize the study of local ontology and moral personhood, in particular, in contexts of transcultural encounters. The focus here is on an incident in a Saharan Tuareg community that is locally-defined as “sorcery” (called ark echaghel in Tamajaq, their Amazigh language). Many Tuareg, predominantly Muslim, traditionally semi-nomadic, and socially-ranked, have experienced socio-economic and ecological upheavals, armed conflicts, and settled life in towns. This analysis examines a case of sorcery practice and its social context—of a transcultural encounter between a smith/artisan and a tourist and its aftermath—of diagnosis and commentary by an Islamic scholar—as moral discourse and local psycho-social treatment as critical commentary on and resistance to transcultural inequalities. The broader goal here is to suggest avenues to pursue by showing how sorcery reveals local ontology, moral discourse on evil, and culture theory. Thus, sorcery, rather than a “retrograde” or irrelevant topic, offers rich insights into local ontology’s psycho-social and political implications, thereby contributing to current concerns in transcultural psychiatry with social and political power asymmetries, critical epistemologies, and indigenous critiques that question universalizing absolutist psychological interpretations in cultural encounters.

Sentimental analysis of social networks: A comprehensive review (2018-2023)

The field of sentiment analysis, a burgeoning field dedicated to deciphering emotions, opinions, and attitudes within text data, has experienced remarkable growth in research interest. This review paper embarks on a comprehensive journey through the dynamic landscape of sentiment analysis, encapsulating its multifaceted dimensions across three prominent academic databases—Scopus, the IEEE Xplore Digital Library, and the Wiley Online Library—spanning from 2018 to 2023. This paper establish the significance of sentiment analysis, charting its evolution from 2008 and elucidating its multifarious applications across domains. We underscore the necessity of drawing insights from diverse databases, transcending the confines of a single source. "On the causality of temporal sentiment analysis," delves into the intricate interplay between sentiment signals and other variables, echoing Granger causality theory from econometrics. We unveil the burgeoning adoption of Granger causality in examining stock market dynamics and sentiment analysis, shedding light on the predictive causality landscape. The temporal dimension takes centre stage "On the temporal dynamics of sentiment analysis," where we explore how sentiment fluctuates over time, particularly during transformative events such as the COVID-19 pandemic. We scrutinize the evolution of emotional appeals in political campaigns and the pivotal role of sentiment analysis in detecting online bullying and suicidal ideation. "On the reproducibility and practical application," we deliberate on the challenges and prospects of implementing sentiment analysis techniques in real-world scenarios. We navigate the intricate landscape of reproducibility and translate research insights into actionable strategies."Discussion and conclusion," encapsulates the essence of our journey. We highlight content variations across databases and unravel the evolution of techniques. We contemplate the bridge between academia and industry by presenting an integrated view of sentiment analysis. As the sentiment analysis field evolves, this review paper offers a panoramic view of its past, present, and future. It is a valuable resource for researchers and practitioners seeking to navigate the ever-expanding horizons of sentiment analysis across diverse databases and domains.

Learning to Double-Check Model Prediction From a Causal Perspective.

The present machine learning schema typically uses a one-pass model inference (e.g., forward propagation) to make predictions in the testing phase. It is inherently different from human students who double-check the answer during examinations especially when the confidence is low. To bridge this gap, we propose a learning to double-check (L2D) framework, which formulates double check as a learnable procedure with two core operations: recognizing unreliable predictions and revising predictions. To judge the correctness of a prediction, we resort to counterfactual faithfulness in causal theory and design a contrastive faithfulness measure. In particular, L2D generates counterfactual features by imagining: "what would the sample features be if its label was the predicted class" and judges the prediction by the faithfulness of the counterfactual features. Furthermore, we design a simple and effective revision module to revise the original model prediction according to the faithfulness. We apply the L2D framework to three classification models and conduct experiments on two public datasets for image classification, validating the effectiveness of L2D in prediction correctness judgment and revision.

Junkies, Queers, and Babies: Persistence and Updating of the Category AIDS Through Silencing and Puncturing of the Moral Boundary

Category emergence has been a central question in organization theory. Yet, we still do not understand what drives category persistence and updating during emergence as new information arises. We investigate this question through a rich set of oral histories and archival materials on the emergence of the category AIDS from 1978 to 1985. We show that the initial proto-category’s features and causal theories cohered with its stigmatized moral meanings. Over time, anomalies challenging these features and theories spurred a minority of medical professionals to update the category’s causal theory. However, a silent majority resisted updating the category because the new causal theory conflicted with the proto-category’s moral meanings, challenging the clear moral boundary dividing what was perceived as worthy from what was perceived as unworthy. As a result, the majority silenced the vocal minority’s updated understandings and withheld resources from the category. This article contributes to the literature on categorization by showing that conflicts between category dimensions can stifle updating, amplify imprinting, and prolong category persistence. Particularly, the dichotomous nature of the moral dimension might hinder the updating of other dimensions, such as the causal one, despite accumulated evidence suggesting the need for updates. We show that calls for category updating backed solely by rational arguments may fail to persuade the silent majority and may falter until the moral boundary is punctured and the category is morally reappraised.

Enhancing Out-of-distribution Generalization on Graphs via Causal Attention Learning

In graph classification, attention- and pooling-based graph neural networks (GNNs) predominate to extract salient features from the input graph and support the prediction. They mostly follow the paradigm of “learning to attend,” which maximizes the mutual information between the attended graph and the ground-truth label. However, this paradigm causes GNN classifiers to indiscriminately absorb all statistical correlations between input features and labels in the training data without distinguishing the causal and noncausal effects of features. Rather than emphasizing causal features, the attended graphs tend to rely on noncausal features as shortcuts to predictions. These shortcut features may easily change outside the training distribution, thereby leading to poor generalization for GNN classifiers. In this article, we take a causal view on GNN modeling. Under our causal assumption, the shortcut feature serves as a confounder between the causal feature and prediction. It misleads the classifier into learning spurious correlations that facilitate prediction in in-distribution (ID) test evaluation while causing significant performance drop in out-of-distribution (OOD) test data. To address this issue, we employ the backdoor adjustment from causal theory—combining each causal feature with various shortcut features, to identify causal patterns and mitigate the confounding effect. Specifically, we employ attention modules to estimate the causal and shortcut features of the input graph. Then, a memory bank collects the estimated shortcut features, enhancing the diversity of shortcut features for combination. Simultaneously, we apply the prototype strategy to improve the consistency of intra-class causal features. We term our method as CAL+, which can promote stable relationships between causal estimation and prediction, regardless of distribution changes. Extensive experiments on synthetic and real-world OOD benchmarks demonstrate our method’s effectiveness in improving OOD generalization. Our codes are released at https://github.com/shuyao-wang/CAL-plus .

Transferring Causal Mechanism over Meta-representations for Target-Unknown Cross-domain Recommendation

Tackling the pervasive issue of data sparsity in recommender systems, we present an insightful investigation into the burgeoning area of non-overlapping cross-domain recommendation, a technique that facilitates the transfer of interaction knowledge across domains without necessitating inter-domain user/item correspondence. Existing approaches have predominantly depended on auxiliary information, such as user reviews and item tags, to establish inter-domain connectivity, but these resources may become inaccessible due to privacy and commercial constraints. To address these limitations, our study introduces an in-depth exploration of Target-unknown Cross-domain Recommendation (CDR), which contends with the distinct challenge of lacking target domain information during the training phase in the source domain. We illustrate two critical obstacles inherent to Target-unknown CDR: the lack of an inter-domain bridge due to insufficient user/item correspondence or side information and the potential pitfalls of source-domain training biases when confronting distribution shifts across domains. To surmount these obstacles, we propose the CMCDR framework, a novel approach that leverages causal mechanisms extracted from meta-user/item representations. The CMCDR framework employs a vector-quantized encoder–decoder architecture, enabling the disentanglement of user/item characteristics. We posit that domain-transferable knowledge is more readily discernible from user/item characteristics, i.e., the meta-representations, rather than raw users and items. Capitalizing on these meta-representations, our CMCDR framework adeptly incorporates an attention-driven predictor that approximates the front-door adjustment method grounded in causal theory. This cutting-edge strategy effectively mitigates source-domain training biases and enhances generalization capabilities against distribution shifts. Extensive experiments demonstrate the empirical effectiveness and the rationality of CMCDR for target-unknown cross-domain recommendation.

Problems of Criminological Determination and Causation in the Scientific Doctrine of Professor Vitaliy Anatolyevich Nomokonov

A famous Russian criminologist, Doctor of Law, Professor, Corresponding Member of the Russian Academy of Natural Sciences, Vice-President of the Russian Criminological Association, author of over 400 research works and textbooks Vitaliy Anatolyevich Nomokonov has celebrated his 75th birthday. He is a well-known figure in both Russian and foreign criminology famous for his works, his conceptual and fundamental scientific views and approaches, unfailing principles and loyalty to universal human values when developing the strategy and tactics of corruption counteraction. Being one of the founders of a highly complex problem area in the field of criminology — the theory of crime determination and causation — he also consistently studies the modern aspects of this theory. The Professor was also one of the first scientists who researched the mechanisms of counteracting organized crime and corruption. He has been studying the regional aspects of these interconnected forms of crime over a long period of time, as well as promoting the ideas of its prevention at the political and public levels. Advocating the proactive development of criminology and its methodological basis along the lines of the transforming society, the Professor has been developing new directions in criminology stressing the anticriminal and anticorruption policy. Positive criminology is a trend in his works, he argues for the humane-personal approach in the theory of the criminal’s personality and proves the importance of understanding the regularities of positive law-abiding behavior and identifying the anti-criminogenic factors underlying it. The number of his students and followers is constantly growing. Over 15 researcher, who wrote their dissertation thesis under V.A. Nomokonov’s supervision, have become the founders of the theory of cybercrime, they promote his ideas regarding anticorruption policy, drug abuse, human trafficking, etc., and have the law enforcement experience that they successfully use in their current work. The Professor’s approaches to understanding organized crime and corruption, as well as the basic and special measures of counteracting these dangerous phenomena, are a safe basis for the corresponding criminological theory, they reflect his forward-thinking and astute attitude. His scientific intuition allows the Professor to be ahead of other research in the sphere of counteracting corruption and provide highly professional predictions on the future regularities of criminal behavior, thus contributing to the development and enriching the theory of criminology and its practical significance.

Enhancing accident cause analysis through text classification and accident causation theory: A case study of coal mine gas explosion accidents

Accident analysis is a crucial aspect of accident prevention, and natural language processing (NLP) techniques can efficiently be applied to analyze the causes of accidents. However, existing analysis methods primarily rely on text clustering and lack the application of accident causation theories, leading to a lack of specific accident cause analysis results. Combining text classification techniques with accident causation theories is an effective approach to address this issue. In this study, we integrated text classification techniques with accident causation theories and utilized coal mine gas explosion accidents as an example. We constructed a corpus, trained a BERT model, and evaluated its performance to obtain a text classification model for accident cause analysis. The results indicated that the BERT model-based text classification algorithm had an accuracy and macro-average F1 value of 0.9878 and 0.7792, respectively, significantly outperforming the control model. The application of this approach demonstrated that combining accident causation theories with text classification techniques for accident cause analysis can improve the efficiency of accident analysis while ensuring the richness of details in analyzing accident causes. By efficiently analyzing a large number of accident cases, this approach can provide a data foundation for data-driven accident prevention and technical support for integrated accident prevention.

Causality in Italian Criminal Law

Modern Italian criminal law doctrine is dominated by the concept of equivalence of conditions as interpreted in the context of «scientific theory» developed in German theory in the 19th century. It is believed that when establishing a causal relationship, it is necessary to use the method of exclusion, focusing primarily on data from the natural sciences, then on data from other sciences and the maxims of general experience. When establishing a causal relationship, the determining factor is the probability of the occurrence of a consequence, while the question of the degree of probability remains controversial. The theories of adequate causation and human causation are rejected by Italian criminal law doctrine. The German theory of objective imputability is considered in the legal literature, it has supporters, but in practice, it is not accepted. The shortcomings of the «scientific theory» of causation are due to methodological errors. Causality is not a scientific category, but a philosophical one. Different sciences, natural and otherwise, have different criteria for establishing a causal relationship that are suitable for these sciences. In the natural sciences, repeatability, which can be assessed statistically, is of decisive importance for establishing a causal relationship. It is impossible to mechanically transfer the methodology of natural sciences to jurisprudence.

Causality between multi-scale built environment and rail transit ridership in Beijing and Tokyo

Recent years have witnessed researchers' academic interests in the relationship between the built environment and rail transit ridership. However, few studies have analyzed it from a beyond-station scale and causal perspective. To fill this gap, this study uses Beijing and Tokyo as cases and utilizes Bayesian networks and generalized propensity score matching to achieve causal discovery and causal inference of built environment and ridership. Moreover, we innovatively consider the line-scale built environment factors. Evidence suggests that transit agencies should promote the overall accessibility of stations along the line to employment concentration areas more than single-station accessibility in Beijing. Meanwhile, the positive impact of bus-rail transit line cooperation on ridership is limited in Beijing. Planners should consider the capacity of rail transit lines. The causal inference results are mainly in line with previous correlation studies, but some interesting differences exist. For example, the net effect of dense pedestrian roads on ridership is negative, reminding policymakers that they should avoid expanding pedestrian paths without developing ridership-attracting resources. This study uses causal theories to emphasize the necessity of corridor-based and modest development.

A Critique of Positivism: Human Nature and Anarchy

Positivism and post positivism are two paradigms within the field of international relations (IR) that differ in their approaches to ontology (the nature of reality) and epistemology (how knowledge is acquired) in the study of international politics. Ontologically, positivists are objectivists, meaning that they believe there are objective facts (for example, selfish human nature) and laws (for example, anarchy leads to chaos) in international politics. Positivists apply common rationality and value-neutrality as epistemological tools to discover and analyze such claims regarding external and objective social reality. In this paper, I make two points. First, positivism (realism and liberalism) has a natural propensity to reify social concepts into trans historical essence to generate causal theories. However, in reality, human nature is complex and the meaning of social realities such as human nature and anarchy is conceptual and constitutive. Second, the adoption of common rationality and value-neutrality as epistemological building blocks lead positivism to a rigid and ahistorical view of human nature and anarchy. In contrast, post positivism (critical theory and constructivism) can generate a relatively nuanced and complete picture of international politics.