Causal Explanations Research Articles

Why ask why? Toward coordinating knowledge of proximate and ultimate explanations in physiology.

In physiology education, students must learn to recognize and construct causal explanations. This challenges students, in part, because causal explanations in biology manifest in different varieties. Unlike other natural sciences, causal mechanisms in physiology support physiological functions and reflect biological adaptations. Therefore, students must distinguish between questions that prompt a proximate or an ultimate explanation. In the present investigation, we aimed to determine how these different varieties of student knowledge coordinate within students' written explanations. Prior research in science education demonstrates that students present specific challenges when distinguishing between proximate and ultimate explanations: students appear to conflate the two or construct other nonmechanistic explanations. This investigation, however, demonstrates that analytic frameworks can distinguish between students' proximate and ultimate explanations when they are provided explanatory scaffolds that contextualize questions. Moreover, these scaffolds and prompts help students distinguish between physiological functions and the cellular and molecular mechanisms that underpin them. Together, these findings deliver insight into the context-sensitive nature of student knowledge in physiology education and offer an analytic framework for identifying and characterizing student knowledge in physiology.NEW & NOTEWORTHY Why ask why? How questions posed in physiology task students with developing their mechanistic reasoning. Why questions sometimes undermine this reasoning. Prior research, however, also illustrates that framing the context of a question explicitly supports students in distinguishing between question types. We further illustrate how providing such context in the form of explanatory scaffolds and prompts allows students to tap different and useful varieties of knowledge when constructing written explanations.

다문화가정 해체와 다문화 조손가정 조모의 양육 경험 : 근거이론을 중심으로

The increase in multicultural families has simultaneously led to an increase in the number of multicultural families broken up due to divorce, bereavement, runaway, etc. The expansion of multicultural families in rural areas has led to the phenomenon of grandmothers taking full responsibility for raising their grandchildren. This study explored the parenting experiences of grandmothers in multicultural families using grounded theory. The core category was ‘Embracing the burden of raising children and finding the meaning of the experience of raising children in old age.’In axial coding, the central phenomenon was 'acquired burden of parenting', the causal condition was 'dissolution of multicultural family', the contextual condition was ‘entrenched maternal instinct', the mediating condition was 'cooperative parenting system', and the action/interaction were ‘the joys and sorrows of parenting’ and ‘grandmother-style parenting strategies’. The result was ‘a life of nurturing that has become a value of existence.’ Upon the understanding the grandmothers’childcare experience through their vivid voices, social welfare support measures were presented.

Does Innovative Work Behavior Matter for Performance? Green Talent Management and Green Servant Leadership in the Healthcare Industry

This study aims to analyze the influence of green talent management and green servant leadership on employee performance while also considering the mediating role of innovative work behavior and the moderating role of green servant leadership in the relationship between green talent management and innovative work behavior. It was carried out in the context of employees in the health industry in Bandung. This study utilized a quantitative approach with a causal explanation to test the hypotheses. The respondents were employees who worked in private hospitals in Bandung, with 190 sample sizes selected using purposive sampling. The data were collected and processed using structural equation modeling with the help of Smart PLS software. The results revealed that green talent management and green servant leadership influenced employees’ innovative work behavior, leading to job performance. Moreover, green servant leadership could also strengthen the influence of green talent management on innovative work behavior. This study contributes to the understanding of the concept of environmentally-friendly business through innovative work behavior so it can improve work performance by adopting green practices, thereby reducing the damage caused by the organization. Furthermore, organizations must also consider that environmentally-friendly business practices are a concern and that a strong sensitivity must be instilled.

Beyond Relevance: Factor-level Causal Explanation for User Travel Decisions with Counterfactual Data Augmentation

Point-of-Interest (POI) recommendation, an important research hotspot in the field of urban computing, plays a crucial role in urban construction. While understanding the process of users’ travel decisions and exploring the causality of POI choosing is not easy due to the complex and diverse influencing factors in urban travel scenarios. Moreover, the spurious explanations caused by severe data sparsity, i.e., misrepresenting universal relevance as causality, may also hinder us from understanding users’ travel decisions. To this end, in this article, we propose a factor-level causal explanation generation framework based on counterfactual data augmentation for user travel decisions, named Factor-level Causal Explanation for User Travel Decisions (FCE-UTD), which can distinguish between true and false causal factors and generate true causal explanations. Specifically, we first assume that a user decision is composed of a set of several different factors. Then, by preserving the user decision structure with a joint counterfactual contrastive learning paradigm, we learn the representation of factors and detect the relevant factors. Next, we further identify true causal factors by constructing counterfactual decisions with a counterfactual representation generator, in particular, it can not only augment the dataset and mitigate the sparsity but also contribute to clarifying the causal factors from other false causal factors that may cause spurious explanations. Besides, a causal dependency learner is proposed to identify causal factors for each decision by learning causal dependency scores. Extensive experiments conducted on three real-world datasets demonstrate the superiority of our approach in terms of check-in rate, fidelity, and downstream tasks under different behavior scenarios. The extra case studies also demonstrate the ability of FCE-UTD to generate causal explanations in POI choosing.

Rethinking rigour to embrace complexity in peacebuilding evaluation

The field of peacebuilding evaluation has evolved over time in response to the complex nature of peace efforts. However, it still predominantly relies on evaluation models that aim to measure discrete peace outcomes adhering to rigid notions of rigour. The inclusive rigour framework presented in this article responds to this challenge, adding to complexity-aware and epistemologically plural approaches to build credible causal explanations in conditions of uncertainty. It identifies three interconnected domains of evaluation design and practice: effective methodological bricolage, meaningful participation and inclusion, utilisation and impact. Rigour here is not defined by methodological choice alone, but rather, relies on an active view of evolving methodological choices throughout an iterative process as maximum use value and meaningful participation are sought. Using three cases, we highlight the critical role of partnership arrangements and associated evaluation cultures and mindsets underpinned by power dynamics that enable or hinder the practice of inclusive rigour.

A phenomenological account for causality in terms of elementary actions

Abstract Discussions on causal relations in real life often consider variables for which the definition of causality is unclear since the notion of interventions on the respective variables is obscure. Asking “what qualifies an action for being an intervention on the variable X X ” raises the question whether the action impacted all other variables only through X or directly, which implicitly refers to a causal model. To avoid this known circularity, we instead suggest a notion of “phenomenological causality” whose basic concept is a set of elementary actions. Then the causal structure is defined such that elementary actions change only the causal mechanism at one node (e.g. one of the causal conditionals in the Markov factorization). This way, the principle of independent mechanisms becomes the defining property of causal structure in domains where causality is a more abstract phenomenon rather than being an objective fact relying on hard-wired causal links between tangible objects. In other words, causal relations between variables get defined by the interface between the system and an external agent (who is able to perform the elementary actions), rather than being an internal property of links between the variables. We describe this phenomenological approach to causality for toy and hypothetical real-world examples and argue that it is consistent with the causal Markov condition when the system under consideration interacts with other variables that control the elementary actions.

Empathy Levels and Burnout in Medical Students: An Analytic Cross-Sectional Study in a Thai University Hospital

Objective: In medical education, empathy is an essential element of professionalism; however, medical students are sometimes advised to limit empathy. Excessive empathy might be linked to burnout and trigger negative consequences such as low mood and quality of life. Due to limited data regarding the association between empathy and burnout, this study aimed to examine the relationship between levels of empathy and burnout with their respective subscales, among medical students.Material and Methods: This cross-sectional study was conducted at the clinical level of medical students currently undergoing medical training at the Faculty of Medicine, Prince of Songkla University, at the end of the 2020 academic year. Medical students aged more than 20 years who completed the questionnaires were included. The questionnaires comprised 1) demographic data, 2) The Toronto Empathy Questionnaire, 3) The Maslach Burnout Inventory (Thai version), and 4) The Thai Mental Health Indicator-15. Associations between empathy and burnout including emotional exhaustion, depersonalisation, and personal accomplishment subscales were investigated using linear regression analysis. Results: From the three-year clinical level, 91.9% (466 of 507) of medical students completed the questionnaires, with a mean age of 23.1±1.4 years. In the linear regression analyses, empathy scores were positively associated with emotional exhaustion and negatively associated with depersonalisation and low personal accomplishment (Adjusted coefficient 0.18 (0.02, 0.33), -0.09 (-0.18, -0.01), and -0.42 (-0.52, -0.31), respectively). Among the empathy subscales, altruism was significantly correlated with personal accomplishment (r=-0.41, p-value<0.001).Conclusion: The study revealed a negative correlation between empathy and overall burnout. While a high level of empathy was found to prevent depersonalisation and enhance personal accomplishment, it did not significantly hinder the emotional exhaustion associated with burnout. Empathy, particularly altruism, was related to personal accomplishments. Our findings suggest that empathy is a crucial determinant of burnout prevention; therefore, optimal levels of empathy should be taught to medical students during medical training to prevent emotional exhaustion. However, the evaluation of further causal explanations is recommended.

‘Almost nothing is firmly established’: A History of Heredity and Genetics in Mental Health Science

Background For more than a century, scientists have tried to find the key to causation of mental ill health in heredity and genetics. The difficulty of finding clear and actionable answers in our genes has not stopped them looking. This history offers important context to understanding mental health science today. Methods This article explores the main themes in research on genetics and inheritance in psychiatry from the second half of the nineteenth century to the present day, to address the question: what is the history of genetics as a causative explanation in mental health science? We take a critical historical approach to the literature, interrogating primary and secondary material for the light it brings to the research question, while considering the social and historical context. Results We begin with the statistics gathered in asylums and used to ‘prove’ the importance of heredity in mental ill health. We then move through early twentieth century Mendelian models of mental inheritance, the eugenics movement, the influence of social psychiatry, new classifications and techniques of the postwar era, the Human Genome Project and Genome Wide Association Studies (GWAS) and epigenetics. Setting these themes in historical context shows that this research was often popular because of wider social, political and cultural issues, which impacted the views of scientists just as they did those of policymakers, journalists and the general public. Conclusions We argue that attempting to unpick this complex history is essential to the modern ethics of mental health and genetics, as well as helping to focus our efforts to better understand causation in mental ill-health.

Relying on LEADER? A place-based policy approach to the rural development of Finnish municipalities

In Finnish rural development, the role of municipalities has proven to be modest, even though the place-based policy approach in principle gives them an important position. The main part of municipal rural development involves cooperation with LEADER groups, to which municipalities give priority. In this study I ask: What explains the current status of municipalities in place-based rural development, that is, the dominance of LEADER and the small scale of the municipalities' own rural development? The methodology is based on critical realism, and the analysis draws from semi-structured interviews conducted in three municipalities. The aforementioned state of local rural development is called demi-regularity, which is the starting point of the research. Using a thematic analysis developed by Tom Fryer for critical realist research, the study proposes three causal explanations for this observation, namely the idea of responsible local communities, the development policy of the shrinking municipality, and the challenges of projectification. According to the findings, the increased role of villages in rural development does not necessarily support the involvement of municipalities, but rather the opposite. The study also shows that when municipalities are primarily looking for (economic) growth, they have difficulties in trusting the potential of rural areas, let alone exploiting it. On the other hand, the challenges of projectification do not characterise LEADER projects in the same way as other municipal project work, making it tempting to rely on LEADER in municipal rural development. Overall, the study shows how strongly and differently LEADER is rooted in place-based rural development in Finland. In order to bring municipalities back into the scene, a broader discussion on the actual preconditions of municipalities in place-based rural development is needed.

Generative adversarial network-enhanced directional seismic wavefield decomposition and its application in reverse time migration

Reverse time migration (RTM) is an accurate method for imaging complex geologic structures without imposing any dip limitations. However, a large amount of high-amplitude low-frequency noise, which is mainly generated by the crosscorrelation of source and receiver wavefields propagating in the same directions, seriously contaminates the image quality. The causal imaging condition with separated up- and downgoing wavefields is an effective approach to reduce these low-frequency artifacts. Explicit up- and downgoing wavefield decomposition based on the Hilbert transform is computationally expensive due to additional wavefield extrapolation and storage for the imaginary parts. Directionally propagating wavefields have distinctive kinematic patterns such as traveltime and wavefront curvature, which provide us an opportunity to implement the wavefield decomposition using the statistical neural network method. Using extrapolated wavefields as the input and the decomposed up-, down-, left-, and rightgoing wavefields as the labeled data, we train a pair of generative adversarial networks to predict the directional wavefields. The training data sets are generated using seismic full-waveform modeling and explicit wavefield decomposition based on the Hilbert transform. Then, the decomposed directional wavefields are incorporated into a novel imaging condition that depends on the subsurface dip angles to compute the reflectivity perpendicular to the reflectors. Numerical experiments demonstrate that our method can produce accurate directional wavefield decomposition results and high-quality reflectivity images without low-wavenumber artifacts.

Stringy spacetime uncertainty principle and a modified trans-Planckian censorship criterion

We study the implications of the stringy spacetime uncertainty relation (STUR) for inflationary cosmology. By demanding that no fluctuation modes that exit the Hubble radius are affected by the nonlocality resulting from the STUR, we find an upper bound on the number of e-foldings of inflation. The bound is a factor of 2 weaker than what results from the trans-Planckian censorship criterion (TCC). By demanding that the inflationary phase is simultaneously consistent with STUR and sufficiently long for inflation to provide a causal explanation of structure on the scale of the current Hubble radius, we find an upper bound on the energy scale of inflation. The bound is less restrictive than what follows from the TCC, but it remains in conflict with canonical single-field inflation models. Published by the American Physical Society 2024

The nature, causes, and consequences of inter-regional inequality

Abstract Social scientists and policymakers alike have become increasingly concerned with understanding the nature, causes, and consequences of inter-regional inequality in economic living conditions. Contemporary spatial inequality is multi-faceted—it varies depending on how we define inequality, the scale at which it is measured, and which groups in the labor force are considered. Increasing economic inequality has important implications for broader social and political issues. Notably, it is difficult to account for the rise of far-right populism in industrialized countries without considering the context of growing inter-regional inequality. Important explanations for the rise in inter-regional inequality include changing patterns of worker and firm sorting processes across space, major transitions like the reorientation of the economy from manufacturing to digital technologies, and increasing global economic integration, as well as policy. Different causal explanations in turn imply a different role for place-based policy. This article introduces the context of the special issue on the nature, causes, and consequences of inter-regional inequality, focusing specifically on inequality in North America and Western Europe, and aims to identify challenges for, and spark further research on, inter-regional inequality.

Knowledge From the Margins – The Epistemological Promise of Microhistorical Theorizing in Organization Studies

This paper proposes a feminist microhistorical approach to theorizing, which can complement the existing critiques remedying the epistemological bias of mainstream theories and conventional management history. Feminist microhistory prompts us to examine particular moments, people, things, or institutions in the past. It is a critical stance toward knowledge production. It aids us in finding and listening to marginal voices of the past, and thus helps in rewriting and re-conceptualizing organizational history or theories. Further, through feminist microhistory, this paper advocates situated theorizing that does not aim at causal explanations or universal knowledge claims but rather understanding the sporadic and multifaceted nature of social life and organizing in specific settings.

Revealing repeat use intention of online food delivery services in Indonesia: what really counts?

ABSTRACT This study intends to explore individual behavior in reusing online food delivery services by examining their satisfaction and perception of other reviews (i.e. bandwagon effect) in Indonesia. The predictors of personal satisfaction (i.e. performance expectancy, habit, food quality, and service fulfillment) were also examined. The Unified Theory of Acceptance and Use of Technology (UTAUT) framework was used as a point of reference combined with the attribution theory to understand how people make causal explanations and use information from others. An explanatory hypothetical-deductive method was adopted by conducting an online survey with a purposive sampling technique targeting individuals who have experienced online food delivery in Indonesia. A total of 262 respondents who completed the questionnaire were considered valid for analysis. WarpPLS 6.0 was used to test the hypotheses. This study found that performance expectancy, habit, food quality, and service fulfillment are significantly good predictors of satisfaction, while satisfaction and bandwagon effect affect repeat use of online food delivery services. In addition, we found that the effect of performance expectancy and food quality on satisfaction is significant for customers who use the online food delivery service less frequently, confirming the law of diminishing marginal utility. Our study offers an integrative framework for understanding consumer behaviors in the context of online food delivery services, which has been partially studied.

A meta‐analysis of SES framework case studies: Identifying dyad and triad archetypes

Abstract There is a need to synthesize the vast amount of empirical case study research on social‐ecological systems (SES) to advance theory. Innovative methods are needed to identify patterns of system interactions and outcomes at different levels of abstraction. Many identifiable patterns may only be relevant to small sets of cases, a sector or regional context, and some more broadly. Theory needs to match these levels while still retaining enough details to inform context‐specific governance. Archetype analysis offers concepts and methods for synthesizing and explaining patterns of interactions across cases. At the most basic level, there is a need to identify two and three independent variable groupings (i.e. dyads and triads) as a starting point for archetype identification (i.e. as theoretical building blocks). The causal explanations of dyads and triads are easier to understand than larger models, and once identified, can be used as building blocks to construct or explain larger theoretical models. We analyse the recurrence of independent variable interactions across 71 quantitative SES models generated from qualitative case study research applying Ostrom's SES framework and examine their relationships to specific outcomes (positive or negative, social or ecological). We use hierarchical clustering, principal component analysis and network analysis tools to identify the frequency and recurrence of dyads and triads across models of different sizes and outcome groups. We also measure the novelty of model composition as models get larger. We support our quantitative model findings with illustrative visual and narrative examples in four case study boxes covering deforestation in Indonesia, pollution in the Rhine River, fisheries management in Chile and renewable wind energy management in Belgium. Findings indicate which pairs of two (dyads) and three (triads) variables are most frequently linked to either positive or negative, social or ecological outcomes. We show which pairs account for most of the variation of interactions across all the models (i.e. the optimal suite). Both the most frequent and optimal suite sets are good starting points for assessing how dyads and triads can fulfil the role of explanatory archetype candidates. We further discuss challenges and opportunities for future SES modelling and synthesis research using archetype analysis. Read the free Plain Language Summary for this article on the Journal blog.

Towards Trustworthy Autonomous Systems via Conversations and Explanations

Autonomous systems fulfil an increasingly important role in our societies, however, AI-powered systems have seen less success over the years, as they are expected to tackle a range of social, legal, or technological challenges and modern neural network-based AI systems cannot yet provide guarantees to many of these challenges. Particularly important is that these systems are black box decision makers, eroding human oversight, contestation, and agency. To address this particular concern, my thesis focuses on integrating social explainable AI with cognitive methods and natural language processing to shed light on the internal processes of autonomous systems in a way accessible to lay users. I propose a causal explanation generation model for decision-making called CEMA based on counterfactual simulations in multi-agent systems. I also plan to integrate CEMA with a broader natural language processing pipeline to support targeted and personalised explanations that address people's cognitive biases. I hope that my research will have a positive impact on the public acceptance of autonomous agents by building towards more trustworthy AI.

Are Mathematical Explanations Causal Explanations in Disguise?

AbstractThere is a major debate as to whether there are non-causal mathematical explanations of physical facts that show how the facts under question arise from a degree of mathematical necessity considered stronger than that of contingent causal laws. We focus on Marc Lange’s account of distinctively mathematical explanations to argue that purported mathematical explanations are essentially causal explanations in disguise and are no different from ordinary applications of mathematics. This is because these explanations work not by appealing to what the world must be like as a matter of mathematical necessity but by appealing to various contingent causal facts.

Causation and the Time-Asymmetry of Knowledge

ABSTRACT This paper argues that the knowledge asymmetry (the fact that we know more about the past than the future) can be explained as a consequence of the causal Markov condition. The causal Markov condition implies that causes of a common effect are generally statistically independent, whereas effects of a common cause are generally correlated. I show that together with certain facts about the physics of our world, the statistical independence of causes severely limits our ability to predict the future, whereas correlations between joint effects make it so that no such limitation holds in the reverse temporal direction. In so far as the fact that our world conforms to the causal Markov condition can itself be explained in terms of the initial conditions of the universe, my view is compatible with Albert’s well-known account of the origins of temporal asymmetries, but also provides a more illuminating way to derive the knowledge asymmetry from those initial conditions.

Studying the Connections between Different Variants of Causal Explanations in the Attributional Style

This paper examined the features of the relationship between several causal explanations differentiated by the personalization effect: opportunity, reinforcement, influence of others, and chance circumstances. Two groups of subjects took part in the event, 244 and 270 people each, respectively. The subjects were asked to fill out a specially designed differential questionnaire and the attributional style and USK questionnaires. In the questionnaire, test takers had to evaluate a list of reasons about the measurements: constancy and globality. To study the structure of connections, the Principal component method and correlation analysis are used. It has been established that all the analyzed causes of negative events are combined into one factor, and the attribution of events into two: a group of internal causes (abilities and efforts) and a group of external consequences (others and accidents). There were also significant correlations between assessments of globality and stability, both internal and external attributions, with optimism. The results indicate the mixed role of externalities for optimism and pessimism.

From regional to global and back again? A future agenda for regional evolution and (de)globalised production networks in regional studies

ABSTRACT This paper builds common grounds for a future research agenda in the regional studies of evolutionary economic geography and global production networks. I put forward two ‘troubling themes’ of (geo)politics and heightened risks as the most disruptive forces in today’s increasingly fragmented global economy and argue for their significance in regional studies throughout the post-pandemic 2020s. Massive global change through the reconfiguration of and strategic (de/re)coupling with global production networks will engender new path formation in regional transformation. In this analytical move from the global ‘back again’ to the regional, there are common questions on epistemology (causal explanations) and substantive issues (network/regional resilience, institutions/the state, inequalities/uneven development and new forms of regional policies) for both communities of researchers.