Causal Theory Research Articles

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.

The evolution of honest and dishonest signals of fighting ability.

Competition over resources is often decided via aggressive interactions, which may or may not escalate to all-out fights. Weapons and body size play important roles in such interactions, as they often provide reliable cues of an individual's fighting ability. In contrast, traits like nonfunctional display "weapons" may dishonestly exaggerate fighting ability in order to intimidate opponents into retreating. Signals used in the context of aggressive interactions potentially evolve via very different mechanisms than courtship signals, but have received far less theoretical attention. Here, we contrast the evolution of honest and dishonest signals of fighting ability using a game-theoretic model. Contests are assumed to consist of three discrete stages: display from a distance, low-intensity physical contact, and fighting. At each stage, contestants evaluate the fighting ability of their opponents in comparison to their own based on body size and an aggressive signal. After making this evaluation, contestants decide whether to escalate the interaction or cede to their opponent. Our model predicts that both honest and dishonest aggressive signals can exaggerate far beyond their ecological optima, but that exaggeration is more pronounced for honest signals. Equilibrium levels of aggressiveness-as measured by individuals' propensity to escalate aggressive interactions to the next stage-are independent of the honesty of signals. We additionally develop a novel approach, based on causal inference theory, to understand how changes in underlying parameters shape the coevolution of multiple traits. We use this approach to study how aggression coevolves with body and signal size in response to changes in the cost of losing a fight.

Effectiveness of rural internships for veterinary students to combat veterinary workforce shortages in rural areas.

Veterinarians are a pivotal force in addressing animal health and welfare surveillance, with a critical role in improving public health security and increasing the profits of livestock farmers. Yet, the veterinary profession is adversely affected by personnel shortages, particularly in rural areas. Since the health of people, animals and their shared environment are interconnected in a One Health perspective, a set of policies are required to ensure public health by attraction and retention of veterinarians in rural areas. In France, a tutored internship programme, financially subsiding students and mentors to execute a training period in remote rural areas, was promoted to better integrate and retain veterinary students ending their veterinary training. This paper aims to evaluate how veterinarians' tutored internships influences students' choices for rural practice, using three different statistical methods derived from causal inference theory. Using survey data for the period 2016-2020, we show that: (i) the average effect of the tutored internship on veterinarians' work in food animal sector is not significant; and that (ii) the tutored internship leads veterinarians with a low share of work in the food animal sector to have a rural practise after they graduated between 13 and 20% greater than those who did not participate in the tutored internship.

Endogeneity and causal attributions in management research: Some reflections and proposals

AbstractThis article explores the pivotal role of causal identification in the domain of management research and its alignment with theory creation. It seeks to stimulate thought about how researchers can approach theories and their causal identification with a review of the canonical methods. The article first addresses the intricacies of identification and the principal methodologies that researchers have recently employed in their investigations. Subsequently, it delves into the inherent costs associated with identification, specifically the need for additional assumptions, and the potential constraints on generalizability. It also promotes a more thoroughly examination of the nature of endogeneity. Finally, it explores how the link between causal identification and theory legitimacy could generate superstitious trajectories.

Phenomenological Approach for the Application of the FOAP Concept of the Vicious Circle of Ill-Health

Background: The Field (F) Observation(O) for Accurate(A) Practicum(P) Concept of Vicious Circle of Ill- Health is an informative and innovative concept that focuses on how human activities within the environment reinforce each other in ways that contribute to vicious circle of ill-health. It is intended to change the perspective of community dwellers to embrace the new ideas of adapting healthy environmental practices to prevent and/or control the vicious circle of ill-health among them.Method: This study used the innovative qualitative research method that focused on the realist and meta-narrative reviews. The following databases were used as search engines: Google Scholar, OpenMD, RefSeek, BASE, Semantic Scholar, Academia, ResarchGate, and Google Search Engine.Result: The four stages of the phenomenological approach to the FOAP concept were vividly explained with stated characteristics. These four stages, when cleverly used, can make huge impacts on the lives of community dwellers. The various risk factors of vicious circle of ill-health, under the illustration of the Causal Theory, were greatly elaborated with all cited evidences to back the various claims.Conclusion: Most Liberians look healthy in an unhealthy environment; they get sick from contracting preventable diseases from the unhealthy environment; they go to hospitals or clinics for quality treatment against the preventable diseases; and they return to the same unhealthy environment that made them sick in the first place. This is the vicious circle of ill-health the FOAP Concept addresses. The FOAP Concept is a novel tool that will help national stakeholders and community dwellers successfully address the issue of unhealthy environmental practices.

Increasing the EBITDA of private equity portfolio company through digital enablement

The study of the role of digital implementation in increasing earnings before interest, taxes, depreciation and amortization (EBITDA) in portfolio companies of private equity funds is a topical issue for the development of strategies and competitiveness. Thus, the main purpose of the study was to determine how private equities increase EBITDA in the portfolio companies via digital enablement. For the purpose of understanding the role of digital enablement strategies in value creation, the critical realism research paradigm was identified as the best-fit philosophy. The critical realism approach was chosen because it advances research methodologies through the development of a qualitative theory of causality. A sample of 26 respondents was used with semi-structured interviews being conducted. The data analysis process involved the identification of 3 themes through thematic analysis. Three themes were identified, namely new digital capabilities to improve top-line growth, driven by the Head of Strategy/Head of Department, and transformative initiatives. The results from all three themes indicate that digital enablement is useful in improving private equity firm revenue and EBITDA levels through digital transformation, improved customer engagement, and identification of market opportunities towards digital data capabilities. The findings show that redefinition is critical to improving EBITDA, as it challenges the existing model and provides a forward-looking approach to strategy formulation. Digital enablement strategies are an unexplored area in the context of private equity firms. The strategies are momentous in improving revenue levels and competitiveness while accentuating the EBITDA rates by cost reduction. The results of this study will be of practical use to both businesses and other economic entities