Causal Processes Research Articles

Amyotrophic lateral sclerosis established as a multistep process across phenotypes.

Given the accepted multistep process of disease causation in amyotrophic lateral sclerosis (ALS), the present study was undertaken to determine the number of steps required for disease onset across each of the ALS phenotypes. Clinical and demographic data were prospectively accumulated using the Australian Motor Neurone Disease Registry (2005-2016), and age-specific incidence rates were calculated. Poisson regression was utilized to assess the relationship between log age-specific incidence and log age of onset, with McFadden's R2 used to assess the goodness of fit of the model. In total, 2647 ALS patients were included, with mean disease-onset age being 62.2 ± 12.1 years. A linear relationship between log incidence and log age was established across ALS phenotypes, with variable slope estimates: bulbar 5.1 (95% confidence interval [CI] 4.6-5.6); cervical 2.7 (95% CI 2.3-3.0); lumbar 3.5 (95% CI 3.2-3.9); flail arm 4.7 (95% CI 3.9-5.5); flail leg 3.6 (95% CI 2.6-4.5); primary lateral sclerosis 2.7 (95% CI 1.8-3.7). Slope estimates were significantly higher in the bulbar compared to the cervical, lumbar and primary lateral sclerosis phenotypes. McFadden's R2 values were >0.4 for all phenotypes indicating excellent model fit. A multistep process has been established across all ALS phenotypes with variable slope estimates, suggesting that the number of steps to develop disease is different across clinical presentations. Identification of mechanisms underlying slope estimate variability could exert pathophysiological significance.

The Role of Environment Social and Governance (ESG) Score To Cost of Debt: Evidence From ASEAN Countries*

ABSTRACTEnvironmental social governance (ESG) disclosure has become an increasingly important component in determining a company's financing cost (cost of debt‐CoD). This phenomenon could be attributed to company reputation development and the rise of socially responsible investing. We contribute to the existing literature by investigating further the causality process of ESG scores to the reduction of CoD particularly in nonfinancial companies in one of the most dynamic world regions: Association of Southeast Asian Nations (ASEAN). To meet our research objective; we applied difference‐in‐difference analysis to a dataset of 135 nonfinancial companies (that published ESG score in Bloomberg) from 6 ASEAN countries, using annual frequency data from 2012 to 2021 (1350 observations). We found that ESG does reduce CoD in the range of 21.3% (measured by interest expense) to 27.7% (measured by credit spread). Our finding is quite solid under an array of robustness checks.

A method for interactive collaboration between different actors: theory of change workshops

Abstract It is well recognised that the involvement of different actors in health research is crucial to the success of projects and interventions, as it increases the likelihood that they will be relevant, impactful and applicable in practice. However, researchers and policy makers often lack tools for the participatory involvement of actors. One way of actively involving actors is the Theory of Change (ToC) method. A ToC is a model of a change process that describes how and why an intervention leads to the intended change or outcome in a given context. It describes the path along which change is expected to occur and provides a framework for investigating whether and how change takes place. It is a systematic methodology that can be used both prospectively and retrospectively for the planning, implementation and evaluation of interventions. The approach recognises that changes in socio-ecological systems are complex and challenging and that the causal processes are often non-linear and intertwined. A ToC workshop begins with the formulation of a clear vision for the desired change and the long-term goal of the intervention. Starting from the long-term goal, the necessary medium-term and short-term goals are then defined in a backward mapping process. Next, indicators, activities and assumptions are defined for each intermediate goal. The results of a ToC workshop can be visualised in a ToC map, which shows the causal pathways, relationships, and connections between the different components of the ToC. This visual representation helps the actors to understand the logic, connections, and assumptions underlying the ToC. This in turn facilitates a shared understanding, collaboration, and ownership among all actors involved. Overall, successful involvement of actors using the ToC method can not only enrich the research process, but ultimately also increase the health benefits of interventions and health policies.

Immunological resilience of a temperate catshark to a simulated marine heat wave.

Marine heatwaves (MHW) have recently been proposed as more relevant in driving population changes than the continuous increase in average temperatures associated with climate change. The causal processes underpinning MHW effects in sharks are unclear but may be linked to changes in fitness caused by physiological trade-offs that influence the immune response. Considering the scarcity of data about the immune response of sharks under anomalous warming events, the present study analyzed several fitness indices and characterized the immune response (in the blood, epigonal organ, liver, spleen, and intestine) of temperate adult small-spotted catsharks (Scyliorhinus canicula) after a 30-day exposure to a Category II MHW. The results indicated that adult small-spotted catsharks have developed coping strategies for the MHW. Specifically, among the 35 parameters investigated, only the gonad-to-body ratio (GBR) and plasma glucose showed significant increases. In contrast, igm and tumor necrosis factor receptor (tnfr) gene expression in blood cells, tnfr in the epigonal organ, and the number of monocytes significantly decreased. Although a decline in immune function in small-spotted catsharks was revealed following the MHW exposure, energy mobilization restored homeostasis and indicated a shift in energy allocation towards reproduction. Group resilience may be due to the variable tolerance of individuals, the phenotypic plasticity of cellular immunity, thermal imprinting, and/or metabolic capacity of the individuals.

Predictability and evolutionary determinism — the search for quantitative explanation

Physics describes reality through the understanding and elucidation of physical laws. From these laws, the universe is predictable. It has indeed been suggested that explanation of observable phenomena comes from our ability to predict these events, through our understanding of physical laws and causal processes. Biological entities operate, of course, through physical and chemical laws. But there exist biological processes that have emergent properties, notably the evolution of adaptation through natural selection, which add an extra dimension to the concept of the understanding of biological phenotypes. The population genetic theory of evolution is mathematically formulated, and contains elements of determinism, typified by the action of natural selection, and elements of randomness, typified by the action of genetic drift and by the randomness of mutation. But is the goal-directed property of natural selection sufficient to conclude that the deterministic elements of the evolutionary process make evolutionary change predictable? And to what extent can we expect 'adaptation' to function as an explanation of biological phenotypes?

Development Path of Macao Tourism Symbiosis Integration from the Configuration Perspective

The concept of symbiotic and integrated development represents the adaptive response of Macao tourism to the necessity of diversified development. It is the inevitable path for Macao tourism to achieve high-quality sustainable development. In accordance with the principles of the symbiosis theory, this paper presents an analytical framework and employs Qualitative Comparative Analysis (QCA) to examine the intricate causal processes that simultaneously influence the symbiosis integration development of Macao tourism. The results of the empirical test demonstrate that: (1) The development of Macao tourism symbiosis and integration is influenced by five factors: external policy (EP), regional integration (RI), new digital technology (DT), gaming industry (GI), and cultural element (CE); (2) There are three configuration paths for Macao’s tourism symbiosis, integration, and high-quality development, including an industrial synergy mode, innovation-leading mode, and value-sharing mode; (3) There are two configuration paths formed by the non-high-quality symbiotic integration of Macao tourism, namely, the government absence type and the market failure type; (4) RI and GI are the core conditions for the symbiotic and integrated development of Macao tourism, and there is a certain substitution relationship between EP, DT, and CE. This study advances the academic understanding of the asymmetric causal relationship between various factors and the symbiotic integration of tourism. It enhances the application of the symbiotic theory in the practise of tourism and reveals differences in the path of Macao tourism development under the combination of multiple factors. The findings provide a theoretical basis for the Macao government to optimise the tourism spatial layout and enhance tourism sustainability, build a multi-level “tourism+” symbiotic industrial structure, and establish a modern tourism governance system.

Timed alignments with mixed moves

We study conformance checking for timed models, that is, process models that consider both the sequence of events that occur, as well as the timestamps at which each event is recorded. Time-aware process mining is a growing subfield of research, and as tools that seek to discover timing-related properties in processes develop, so does the need for conformance-checking techniques that can tackle time constraints and provide insightful quality measures for time-aware process models. One of the most useful conformance artefacts is the alignment, that is, finding the minimal changes necessary to correct a new observation to conform to a process model. In this paper, we extend the notion of timed distance from a previous work where an edit on an event’s timestamp came in two types, depending on whether or not it would propagate to its successors. Here, these different types of edits have a weighted cost each, and the ratio of their costs is denoted by α. We then solve the purely timed alignment problem in this setting for a large class of these weighted distances (corresponding to α∈{1}∪[2,∞)). For these distances, we provide linear time algorithms for both distance computation and alignment on models with sequential causal processes.

Addressing causality: participatory evaluation on improvisational drama workshops for people with dementia and their carers

ABSTRACT Despite advancements in arts and health research, the processes through which arts activities contribute to wellbeing and care remain ambiguous. This study aims to shed new light on causal links surrounding the impact of improvisational drama workshops on people with dementia and their carers at care facilities, using both participatory and goal-free evaluation. The article outlines challenges and strategies in addressing causality in the arts, followed by methodology and research context. Findings are discussed framed by key considerations for arts impact: negative impacts, uniqueness of the arts, artistic quality, and long-term outcomes. We conclude that a two-layered causal process, micro and macro, contributes to meaningful impact. This perspective emphasizes learning from negative impacts, integrating artistic elements with other relevant factors through trial and error, and reassessing shared goals during implementation. Sustained efforts in this endeavor can lead to significant long-term outcomes, extending beyond individual behavioral changes to community growth.

Iron Chelators and Alzheimer's Disease Clinical Trials.

Alzheimer's disease (AD) is a major neurodegenerative disorder impacting millions of people with cognitive impairment and affecting activities of daily living. The deposition of neurofibrillary tangles of hyperphosphorylated tau proteins and accumulation of amyloid-β (Aβ) are the main pathological characteristics of AD. However, the actual causal process of AD is not yet identified. Oxidative stress occurs prior to amyloid Aβ plaque formation and tau phosphorylation in AD. The role of master antioxidant, glutathione, and metal ions (e.g., iron) in AD are the frontline area of AD research. Iron overload in specific brain regions in AD is associated with the rate of cognitive decline. We have presented the outcome from various interventional trials involving iron chelators intended to minimize the iron overload in AD. To date, however, no significant positive outcomes have been reported using iron chelators in AD and warrant further research.

Rethinking animal attrition in preclinical research: Expressing causal mechanisms of selection bias using directed acyclic graphs.

Animal attrition in preclinical experiments can introduce bias in the estimation of causal treatment effects, as the treatment-outcome association in surviving animals may not represent the causal effect of interest. This can compromise the internal validity of the study despite randomization at the outset. Directed Acyclic Graphs (DAGs) are useful tools to transparently visualize assumptions about the causal structure underlying observed data. By illustrating relationships between relevant variables, DAGs enable the detection of even less intuitive biases, and can thereby inform strategies for their mitigation. In this study, we present an illustrative causal model for preclinical stroke research, in which animal attrition induces a specific type of selection bias (i.e., collider stratification bias) due to the interplay of animal welfare, initial disease severity and negative side effects of treatment. Even when the treatment had no causal effect, our simulations revealed substantial bias across different scenarios. We show how researchers can detect and potentially mitigate this bias in the analysis phase, even when only data from surviving animals are available, if knowledge of the underlying causal process that gave rise to the data is available. Collider stratification bias should be a concern in preclinical animal studies with severe side effects and high post-randomization attrition.

Improvement of decadal predictions of monthly extreme Mei-yu rainfall via a causality guided approach

Abstract While the improved performance of climate prediction systems has allowed better predictions of the East Asian Summer Monsoon rainfall to be made, the ability to predict extreme Mei-yu rainfall (MYR) remains a challenge. Given that large scale climate modes (LSCMs) tend to be better predicted by climate prediction systems than local extremes, one useful approach is to employ causality-guided statistical models (CGSMs), which link known LSCMs to improve MYR prediction. However, previous work suggests that CGSMs trained with data from 1979–2018 might struggle to model MYR in the pre-1978 period. One hypothesis is that this is due to potential changes in causal processes, which modulate MYR in different phases of the multidecadal variability, such as the Pacific decadal oscillation (PDO). In this study, we explore this hypothesis by constructing CGSMs for different PDO phases, which reflect the different phases of specific causal process, and examine the difference in quality as well as with respect to difference drivers and thus causal links between CGSMs of different PDO phases as well as the non-PDO phase specific CGSMs. Our results show that the set of predictors of CGSMs is PDO phase specific. Furthermore, the performance of PDO phase specific CGSMs are better than the non-PDO phase specific CGSMs. To demonstrate the added value of CGSMs, the PDO phase specific versions are applied to the latest UK Met Office decadal prediction system, DePreSys4, and it is shown that the root-mean squared errors of MYR prediction based on PDO phase specific CGSMs is consistently smaller than the MYR predicted based on the direct DePreSys4 extreme rainfall simulations. We conclude that the use of a causality approach improves the prediction of extreme precipitation based solely on known LSCMs because of the change in the main drivers of extreme rainfall during different PDO-phases.

The Influence of Motivation and Work Stress on the Performance of PT Karya Bakti Metalasri Employees

This research aims to determine the effect of motivation and work stress on employee performance at PT Karya Bakti Metalasri Surabaya (Surabaya and Jakarta branches). The method used is quantitative research with associative causal type and data processing using SPSS version 25. There are 96 samples in this research with the employee criteria being employeeswho work at the DKI Jakarta and Surabaya branches of PT Karya Bakti Metalasri obtained through the census method. The data collection technique in this research uses a questionnaire whose validity and reliability have been tested. The method used is multiple linear regression. The results of this research show that through the F Test (simultaneous test) carried out, it can be concluded that work motivation and work stress have an impact on the performance of PT Bakti Metalasri employees. Through the T Test (partial test), the Work Motivation variable is able to directly influence employee performance. Likewise, the Job Stress variable is able to directly influence employee performance. Furthermore, based on the R-Square Test, the two independent variables, namely Work Motivation and Work Stress, were able to explain the dependent variable Employee Performance by 73.5% at PT. Karya Bakti Metalasri.

IR, climate politics, and change: opportunities for productive engagement?

‘Change’ or ‘transformation’ are longstanding preoccupations of both International Relations (IR) and global climate change politics scholarship. Yet, the two fields largely occupy independent axiological, epistemological, normative, and ontological spaces that have led to misunderstandings, mutual criticisms, and a lack of serious engagement on these questions. The result is missed opportunities to transform IR, misdiagnoses of political dynamics of climate change, and, perversely, the limited influence of political analysis on wider climate change scholarship. This article identifies understandings of change and transformation relevant to both fields and introduces a productive epistemological and ontological shift for analyzing and normatively engaging with change in the face of uncertainty. It then introduces practical research strategies for policy-relevant and forward-looking scholarship that moves from explaining change to identifying causal logics and dynamic processes that can reinforce (or undermine) change and transformation. It concludes with illustrative analyzes of trajectories and possible limits of two macro policy changes with transformative potential: the 1.5-degree Celsius aspirational target in the Paris Agreement, and the proliferation of ‘net zero’ policies around the world.

Development of hepatic fibrosis in common variable immunodeficiency-related porto-sinusoidal vascular disorder.

Liver involvement is an increasingly recognised complication of common variable immunodeficiency (CVID). Nodular regenerative hyperplasia (NRH), a subgroup of porto-sinusoidal vascular disorder, and manifestations of portal hypertension (PH) unrelated to cirrhosis are the most common findings. Nonetheless, the evolution of liver disease over time remains unknown. Retrospective review of patients followed at the National Institutes of Health with CVID-related liver disease and liver biopsy from 1990 to 2020. Clinical, imaging and histological follow-up were recorded as part of clinical research protocols. Forty patients were included, with a median age of 37.5 years at initial biopsy, 73% presenting with clear evidence of NRH, and a median fibrosis stage of 1. At biopsy, median platelet count was 100 × 109/L, spleen size 19.5 cm, hepatic venous pressure gradient 9.5 mmHg and 37.5% of patients had signs of PH. Cumulative incidence of PH was 65% at 5 years. In a subgroup of 16 patients, a follow-up liver biopsy, performed at a median time of 3 years after the index biopsy, revealed an increase in fibrosis by ≥2 stages in 31% of cases and an increase to an overall stage of 2.2 (p = 0.001). No clinical or histological factors were associated with progression of fibrosis. In this CVID cohort, NRH is the most common initial histological finding; however, unexpectedly fibrosis progresses over time in a subgroup of patients. A better understanding of the underlying causal process of liver disease CVID might lead to improved outcomes.

Inference with cross-lagged effects-Problems in time.

The interpretation of cross-effects from vector autoregressive models to infer structure and causality among constructs is widespread and sometimes problematic. I describe problems in the interpretation of cross-effects when processes that are thought to fluctuate continuously in time are, as is typically done, modeled as changing only in discrete steps (as in e.g., structural equation modeling)-zeroes in a discrete-time temporal matrix do not necessarily correspond to zero effects in the underlying continuous processes, and vice versa. This has implications for the common case when the presence or absence of cross-effects is used for inference about underlying causal processes. I demonstrate these problems via simulation, and also show that when an underlying set of processes are continuous in time, even relatively few direct causal links can result in much denser temporal effect matrices in discrete-time. I demonstrate one solution to these issues, namely parameterizing the system as a stochastic differential equation and focusing inference on the continuous-time temporal effects. I follow this with some discussion of issues regarding the switch to continuous-time, specifically regularization, appropriate measurement time lag, and model order. An empirical example using intensive longitudinal data highlights some of the complexities of applying such approaches to real data, particularly with respect to model specification, examining misspecification, and parameter interpretation. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The Nature of Pointer States and Their Role in Macroscopic Quantum Coherence

This article begins with an interdisciplinary review of a hydrodynamic approach to understanding the origins and nature of macroscopic quantum phenomena in high-temperature superconductivity, superfluidity, turbulence and biological systems. Building on this review, we consider new theoretical insights into the origin and nature of pointer states and their role in the emergence of quantum systems. The approach includes a theory of quantum coherence underpinned by turbulence, generated by a field of pointer states, which take the form of recirculating, spin-1/2 vortices (toroids), interconnected via a cascade of spin-1 vortices. Decoherence occurs when the bosonic network connecting pointer states is disrupted, leading to their localisation. Building further on this work, we explore how quantum particles (in the form of different vortex structures) could emerge as the product of a causal dynamic process, within a turbulent (fractal) spacetime. The resulting particle structures offer new insights into intrinsic spin, the probabilistic nature of the wave function and how we might consider pointer states within the standard “point source” representation of a quantum particle, which intuitively requires a more complexed description.

Major Problems in Clinical Psychological Science and How to Address them. Introducing a Multimodal Dynamical Network Approach

Abstract Background Despite impressive dissemination programs of best-practice therapies, clinical psychology faces obstacles in developing more efficacious treatments for mental disorders. In contrast to other medical disciplines, psychotherapy has made only slow progress in improving treatment outcomes. Improvements in the classification of mental disorders could enhance the tailoring of treatments to improve effectiveness. We introduce a multimodal dynamical network approach, to address some of the challenges faced by clinical research. These challenges include the absence of a comprehensive meta-theory, comorbidity, substantial diagnostic heterogeneity, violations of ergodicity assumptions, and a limited understanding of causal processes. Methods Through the application of multimodal dynamical network analysis, we describe how to advance clinical research by addressing central problems in the field. By utilizing dynamic network analysis techniques (e.g., Group Iterative Multiple Model Estimation, multivariate Granger causality), multimodal measurements (i.e., psychological, psychopathological, and neurobiological data), intensive longitudinal data collection (e.g., Ecological Momentary Assessment), and causal inference methods (e.g., GIMME), our approach could improve the comprehension and treatment of mental disorders. Under the umbrella of the systems approach and utilizing e.g., graph theory and control theory, we aim to integrate data from longitudinal, multimodal measurements. Results The multimodal dynamical network approach enables a comprehensive understanding of mental disorders as dynamic networks of interconnected symptoms. It dismantles artificial diagnostic boundaries, facilitating a transdiagnostic view of psychopathology. The integration of longitudinal data and causal inference techniques enhances our ability to identify influential nodes, prioritize interventions, and predict the impact of therapeutic strategies. Conclusion The proposed approach could improve psychological treatment by providing individualized models of psychopathology and by suggesting individual treatment angles.

Event Causality Identification via Competitive-Cooperative Cognition Networks

Identifying the causal relations between events is an important task in natural language processing (NLP). However, existing methods mainly leverage human empirical information about causality from limited labeled corpus or pseudo-data, which leads to an insufficient understanding of the causal mechanism. Inspired by the dual process theory (DPT) in cognitive science, we propose Competitive-Cooperative Cognition Networks (C3Net), including an intuitive causal reasoner, a logical causal reasoner, and a competitive selection mechanism, which directly learn and imitate the framework of the causal identification process in human brains. On the one hand, our model can inject external causal knowledge into the intuitive causal reasoner to improve the performance of intuitive reasoning. On the other hand, it can make explicit logical reasoning in a three-stage paradigm, mimicking human reasoning behavior: association→review→inference. Furthermore, we devise a novel competitive selection mechanism based on private evidence and historical patterns of each reasoner to determine which reasoner’s inference is more reliable. Experiments demonstrate that our approach can achieve better performance than previous baseline methods.

The State of Nationalism (SoN): Nationalism and International Conflict

Does nationalism increase the probability of international conflict? An affirmative answer has intuitive appeal: Nationalists promote force to protect their land and people from foreign threats. Research on nationalism in international conflict implicates elites, masses, and political interactions between these actors when asserting its status as a powerful force that raises the risk for interstate wars. Yet both micro- and macro-level evidence cautions against drawing broad conclusions about how nationalism affects conflict. Nationalism sometimes prompts aggression. In other contexts, nationalism produces restraint. Systematic knowledge about how nationalism relates to international conflict therefore requires answers to questions about which nationalisms promote conflict and whose nationalism matters. This article first reviews theory and evidence about nationalism’s conflict-causing potential. We discuss the microfoundations that link nationalism to foreign policy attitudes before describing research that implicates nationalism as a cause of militarized conflict. Next, we argue that understanding the complex relationships between nationalism and conflict requires theorizing the causal processes that connect various stakeholders’ nationalism to foreign policy outcomes. Moreover, research on identity content suggests that nationalism is compatible with a variety of foreign policy preferences. Throughout, we centre research that features nationalism as a principal independent variable or causal mechanism precipitating conflict. Any review about nationalism must contend with the delicate matter of conceptualization — the field contains nearly as many definitions for nationalism as articles about nationalism and war. The research we discuss variously describes nationalism as an identity, ideology, or belief, for example. For our purposes, nationalism captures a set of sentiments related to a national group and its superiority. This broad perspective centers the phenomenon implicated in international conflict research.

The interplay between teleworking choice and commute distance

In this study, we model the telework-commute distance decision process as a package choice, and use a latent segmentation approach to recognize that some individuals may choose their telework arrangement first and then choose their commute distance (their residential location), while others may adopt a reverse causal behavioral process. The latent segment modeling methodology combines a multiple discrete–continuous probit model for telework adoption/intensity as the proportion of monthly workdays worked from home, from the office, and from a variable third workplace, with a log-linear regression model for commute distance (the home-to-office distance). The data for our analysis is drawn from a 2022 survey of Texas residents. Results suggest that about 20% of the population are likely “movers”, whose telework choices led to a residential move that changed commute distance, while the other 80% are “stayers”, whose commute distance at their predetermined (to telework) residential location changed telework habits. We further quantify the impact of teleworking on commute distances by computing the expected commute distance for several telework distribution preferences. A shift from 100% in-person work to 100% home-based remote work increases average commute distance by 64.8%, while working from a third workplace for any portion of a month reduces commute lengths relative to only home-based telework. In terms of monthly commute vehicle miles traveled (VMT), working from home for less than 18.6% of the month (about once a week) per worker actually increases overall monthly commute VMT. Monthly commute VMT reductions become tangible only at about 30–40% telework for each worker (about two days of telework per week for full-time workers with 22 workdays per month). In general, between 0 and 40% home-based telework per worker (with the remaining percentage at the office), there is a 10% commute VMT reduction for each 10% increase in home-based telework. In the range of 40% to 80% home-based telework, there is about a 13% commute VMT reduction for every 10% increase in home-based telework. And in the range of 80%-100% home-based telework reduction, there is about a 15% commute VMT reduction for every 10% increase in home-based telework.