Causal Theory Research Articles

Adopted and donor-conceived children hold reduced genetic-essentialist beliefs relative to traditionally-conceived children.

For three decades, researchers have assessed children's genetic essentialism through an "Adoption Task," probing their beliefs about whether adopted babies grow up to resemble their birthparents or adoptive parents. The present study investigates these beliefs among children who were themselves adopted or donor-conceived (i.e., who share genetic material with neither or one parent, respectively; "ADC"), comparing them with children who were traditionally-conceived (i.e., who share genetic material with two parents; "TC"). Children (n = 95, 4-8 years old, 30ADC/65TC, 34M/59F/2NB, 55 White / 35 of color / five did not report) completed five trials of an Adoption Task concerning five personal characteristics (hair color, spoken language, personality, interests, and intelligence). Across trials, ADC children made fewer essentialist judgments than TC children (ηp² = .07); the odds of an essentialist response were threefold greater among TC than ADC participants. Exploratory analyses revealed that with age, children's essentialist judgments became increasingly differentiated across different characteristics (e.g., increasingly divergent views of the genetic basis of hair color vs. spoken language), and that this differentiation was more pronounced in the ADC than the TC sample. No differences were observed in the genetic-essentialist views of ADC and TC children's parents, suggesting that the mechanism underlying effects was children's own sense-making capacities. Whereas nature and nurture are confounded for TC children, these factors are decoupled for ADC children, supporting them in building their causal theories. Cognitive, developmental, and social implications are discussed, along with translational implications for ADC children and their families. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Research on safety risk factors of metro shield tunnel construction in China based on social network analysis

PurposeDuring metro construction using the shield method, the construction process's complexity, the construction environment's variability, and other factors can easily lead to tunnel construction accidents. This paper aims to explore the interconnections between risk factors and related accident types, as well as the risk chain formed between risk factors, and to analyze the key risk factors and vulnerabilities in shield tunnel construction through empirical data.Design/methodology/approachBased on the social network analysis theory, the connection of various risk factors in subway shield tunnel construction is explored, and the mechanism of multiple risk factors is studied. Through literature analysis, articles on safety risk factors in metro shield tunnel construction are organized and studied, and the identified safety risk factors can comprehensively reflect the significant risks that need to be concerned in metro shield tunnel construction.FindingsThe results show that a small world characterizes the SNA network of safety risk factors for metro shield tunnel construction: The frequency of association between the five risk factors “unsafe behavior,” “site management,” “safety supervision and inspection,” “safety education system” and “safety protection” is higher than that of other factors. Only a few risks, such as “site management,” “safety supervision and inspection,” and “rapid response capability,” directly lead to accidents. In addition, risk factors such as the “safety education system” and “safety protection” will indirectly cause unsafe behaviors of construction personnel.Research limitations/implicationsDuring construction, the probability of occurrence of risk factors may vary with the construction phase and area and is not considered in this paper. In addition, although this paper identifies, determines and analyzes the risk factors affecting the safety of metro shield tunnel construction, including the importance of each risk factor and the connection between them, more detailed information before and after the accident could not be obtained based on the accident investigation report alone. Therefore, future research can collect the same accident case from more sources to obtain more information.Practical implicationsThe theory of accident causation has been improved at the theoretical level. The identified safety risk factors can comprehensively reflect the significant risks that need to be paid attention to in metro shield tunnel construction. From a practical point of view, the results of the study provide a basis for the rational control of the risk factors in the construction of subway shield tunnels, which can help guide practitioners to do a good job of risk prevention before the construction of metro shield tunnels and reduce the probability of related accidents.Originality/valueThis study expands the application of social network analysis in the field of subway tunnel construction risk, quantitatively analyzes the key risk factors and vulnerabilities in shield method tunnel construction and proposes policy recommendations for future metro tunnel construction safety management.

EcolRxC: Ecological inference estimation of R × C tables using latent structure approaches

Abstract Ecological inference is a statistical technique used to infer individual behavior from aggregate data. A particularly relevant instance of ecological inference involves the estimation of the inner cells of a set of R × C related contingency tables when only their aggregate margins are known. This problem spans multiple disciplines, including quantitative history, epidemiology, political science, marketing, and sociology. This paper proposes new models for solving the problem using the latent structure theory, and presents the ecolRxC package, an R implementation of this methodology. This article exemplifies, explains, and statistically documents the new extensions and, using real inner cell election data, shows how the new models in ecolRxC lead to significantly more accurate solutions than ecol and VTR, two Stata routines suggested within this framework. ecolRxC also holds its own against ei.MD.bayes and nslphom, the two algorithms currently identified in the literature as the most accurate to solve this problem. ecolRxC records accuracies as good as those reported for ei.MD.bayes and nslphom. Besides, from a theoretical perspective, ecolRxC stands up for modeling a causal theory of political behavior to build its algorithm. This distinguishes it from other procedures proposed from different frameworks (such as ei.MD.bayes and nslphom) which model expected behaviors, instead of modeling how voters make choices based on their underlying preferences as ecolRxC does.

Do News Reports and Google Searches Impact Bitcoin Prices? An Analysis of Granger Causality

Objective: to analyze the Granger causality relationship between the number of Bitcoin news articles, the number of Google searches related to the term "Bitcoin," and the price of the cryptocurrency. The study aims to understand how these variables influence each other using an econometric time series model. Theoretical framework: the paper is based on studies on financial market efficiency, cryptocurrencies, and Granger causality theory. It reviews research that explores the impact of media information and online searches on the price of financial assets, such as Bitcoin, while also discussing Bitcoin’s role as both a speculative asset and a transactional currency. Method: the construction of a time series with weekly data, from 2015 to 2018, covering Bitcoin price, the number of news articles, and Google searches. The Vector Autoregressive (VAR) model was used to conduct the Granger causality test and verify if there are causal relationships between these variables. Results and Discussion: the findings indicate a triple causality relationship among the studied variables. Bitcoin's price influences both the number of news articles and Google searches, and in turn, the articles and searches also influence Bitcoin's price. This suggests that increased interest and media coverage of Bitcoin drive its demand and price. Research implications: the research suggests that Bitcoin's price behavior is influenced not only by traditional economic factors but also by media information and the volume of online searches. This highlights the importance of informational factors in the cryptocurrency market and the influence of digital media on asset pricing. Originality/value: the study proposes integrating data from news articles and Google searches with Bitcoin price behavior, applying Granger causality analysis. Its contribution lies in demonstrating the importance of media in the dissemination and pricing of cryptocurrencies, providing a new perspective for the literature on digital assets and speculative behavior.

Nightmares in the elderly: Associations with self-reported executive functions

Study objectivesTo describe nightmare phenomenology among community dwelling elderly and to test the hypothesis that reduction in cognitive control is associated with nightmare-related phenomenology including nightmare frequency and severity, greater emotional reactivity, imagery immersion, and dream enactment behaviors (DEBs). MethodsStudy 1: Survey with multiple regression and ANOVAs on N = 56 people with frequent nightmares plus N = 62 age- and gender-matched controls to quantify the strength of the association between cognitive control variables and nightmare phenomenology. Study 2: Computational simulation of nightmare phenomenology in relation to cognitive control to simulate the empirical findings and to assess the underlying causal theory through computationally supported causal inference. ResultsStudy 1: Regressions demonstrated a strong association between reduction in cognitive control and more extreme nightmare phenomenology, including severity, frequency, daytime effects, and DEBs. Study 2: The computational simulation of nightmare phenomenology in relation to cognitive control is validated relative to regressions from study 1 and offers computational support for the causal theory explaining the associations in study 1. ConclusionsIn aging people, decline in executive cognitive functions, cognitive control, and inhibitory processes reduce cognitive control over emotions, thus contributing to unusual nightmare activity, including more extreme nightmare phenomenology such as more severe nightmares, greater emotional reactivity, deeper imagery immersion, and DEBs.

Expanding the Causal Menu: An Interventionist Perspective on Explaining Human Behavioral Evolution

Abstract Theorists of human evolution are interested in understanding major shifts in human behavioral capacities (e.g., the creation of a novel technological industry, such as the Acheulean). This task faces empirical challenges arising both from the complexity of these events and the time-depths involved. However, we also confront issues of a more philosophical nature, such as how to best think about causation and explanation. This article considers such fundamental questions from the perspective of a prominent theory of causation in the philosophy of science literature, namely, the interventionist theory of causation. A signature feature of this framework is its recognition of a family of distinct types of causes. We set out several of these causal notions and show how they can contribute to explaining transitions in human behavioral complexity. We do so, first, in a preliminary way, and then in a more detailed way, taking the origins of behavioral modernity as our extended case study. We conclude by suggesting some ways in which the approach developed here might be elaborated and extended.

Aspects of Everpresent Λ. Part II. Cosmological tests of current models

This paper investigates Everpresent Λ, a stochastic dark energy model motivated by causal set theory and unimodular gravity, and confronts it with two key observational data sets, Supernova Ia (SN Ia) and Cosmic Microwave Background (CMB) data. A key feature of this model is that Λ fluctuates over time and on average the magnitude of its fluctuations is of the order of the dominant energy density (be it radiation or matter) for the given epoch. In particular, we focus on a phenomenological implementation of Everpresent Λ known as Model 1. The random fluctuations in Everpresent Λ realizations are generated using seed numbers, and we find that for a small fraction of seeds Model 1 is capable of producing realizations that fit SN Ia data better than ΛCDM. We further investigate what features distinguish these realizations from the more general behaviour, and find that the “good” realizations have relatively small fluctuations at low redshifts (z < 1.5), which do not closely track the matter density. We find that Model 1 struggles to improve on ΛCDM at describing the CMB data. However, by suppressing the values of Λ near the last scattering surface, as suggested in [1], we find a large improvement in the best fit of the model, though still with a χ 2 value much larger than that of ΛCDM. We also study the allowed variation of the dark energy density by the CMB constraints in a more model-independent manner, and find that some variation (especially prior to recombination) is possible and in fact can lead to improvement over ΛCDM and reduce the Hubble tension, in line with some early dark energy proposals. However, for the kinds of variations considered, the favoured fluctuations are smaller in magnitude than is typical in current Everpresent Λ models.

Poverty Reduction and Family Functioning: Results from an Experimental Study in Sub-Saharan Africa

The study tests the effect of poverty-reduction intervention on family functioning reported by AIDS-orphaned children in extended families in Southern Uganda by asking two questions: (1) based on children’s reports, how does poverty reduction intervention affect family functioning? and (2) to what extent do these effects vary by a child’s gender and orphanhood status? Informed by the social causation theory, family stress model, and asset theory, the study aims to address the existing knowledge gap on effects of poverty reduction interventions on family functioning in low-income families caring for AIDS orphaned children in Uganda. We ran multilevel regression models using longitudinal data collected in a cluster-randomized controlled trial from N = 1410 children (n = 621 boys and n = 789 girls) recruited from 48 rural primary schools in Uganda. Survey data was collected every 12 months over the course of 5 years. The average age of children at enrollment was 13 years. We found significant positive effects of the intervention on family cohesion, family communication, and child-caregiver relationship. Effects vary by child’s gender and orphanhood category. Intervention improves family communication for boys, while improving family cohesion and quality of child-caregiver relationship for girls. Single maternal orphans reported improved family communication, while single paternal orphans reported improved child-caregiver relationship. Poverty reduction interventions are important to improve family functioning for low-income families. Variations by child’s gender and orphanhood status have not been reported in previous studies, and our findings underscore the importance of continued research in this area.

Physical influx theory: the case of Émilie Du Châtelet

ABSTRACT In this paper, I analyse Émilie Du Châtelet’s (1706–49) account of causation which, in turn, is crucial for understanding her philosophical main work, the Institutions de physique (1740/1742). So far, the topic of causation in Du Châtelet’s thought has received but little attention despite its importance. I will show that Du Châtelet’s account of physical causation is that of physical influx much in line with the position taken by some of the most prominent eighteenth-century German metaphysicians at the time of the publication of her Institutions: Martin Knutzen (1713–49), Johann Peter Reusch (1691–1758) and the pre-Critical Immanuel Kant (1724–1804). I will argue that physical influx takes place at the level of simple substances or physical monads, and I will defend my interpretation against the main objection to physical influx: that it conflicts with Leibniz’s non-transfer argument. The ultimate metaphysical details regarding how physical influx has to be understood remain an issue, that of understanding the causality of the cause. This, however, is an issue that other causal theories face, too (in their own way).

Single World Intervention Graphs (SWIGs): A Practical Guide.

This article offers a comprehensive and user-friendly guide to visualizing causal theories using Single World Intervention Graphs (SWIGs). We begin with a discussion of the potential outcomes approach to causality and limitations of using Directed Acyclic Graphs (DAGs) under this framework. We then introduce SWIGs as a simple but powerful tool for integrating potential outcomes explicitly into causal diagrams. The article provides a step-by-step guide on transforming DAGs into SWIGs that includes practical insights into constructing SWIGs under various scenarios such as confounding, mediation, and sequential randomization. Highlighting the utility of SWIGs in practice, we illustrate their application in identifying the g-formula, showcasing their capacity to make causal estimands visually explicit. This article serves as a resource for epidemiologists and researchers interested in expanding their causal inference toolkit.

The Sure Thing Principle leads to instability

Abstract Orthodox causal decision theory is unstable. Its advice changes as you make up your mind about what you will do. Several have objected to this kind of instability and explored stable alternatives. Here, I’ll show that explorers in search of stability must part with a vestige of their homeland. There is no plausible stable decision theory which satisfies Savage’s Sure Thing Principle. So those in search of stability must learn to live without it.

Evidence, Causality, and Collective Action

Abstract In collective action problems, large numbers of contributions together produce a good outcome, but any one contribution often makes no difference. Many philosophers think that act consequentialism implies that individuals should not contribute in these cases, given that their contributions cannot be expected to affect the outcome. Nearly everyone has assumed that the relevant expected effects of an action are those effects that are counterfactually dependent on what a given agent does. This assumption is at the heart of causal decision theory. In contrast, evidential decision theory evaluates actions on the basis of whatever can be expected to happen, given that the agent performs some action, where this includes events that are simply correlated with the action. I show that causal and evidentialist versions of act consequentialism can diverge in collective action problems. Evidential decision theory can require us to do our part even when causal decision theory does not.

A hybrid constrained continuous optimization approach for optimal causal discovery from biological data.

Understanding causal effects is a fundamental goal of science and underpins our ability to make accurate predictions in unseen settings and conditions. While direct experimentation is the gold standard for measuring and validating causal effects, the field of causal graph theory offers a tantalizing alternative: extracting causal insights from observational data. Theoretical analysis has shown that this is indeed possible, given a large dataset and if certain conditions are met. However, biological datasets, frequently, do not meet such requirements but evaluation of causal discovery algorithms is typically performed on synthetic datasets, which they meet all requirements. Thus, real-life datasets are needed, in which the causal truth is reasonably known. In this work we first construct such a large-scale real-life dataset and then we perform on it a comprehensive benchmarking of various causal discovery methods. We find that the PC algorithm is particularly accurate at estimating causal structure, including the causal direction which is critical for biological applicability. However, PC does only produces cause-effect directionality, but not estimates of causal effects. We propose PC-NOTEARS (PCnt), a hybrid solution, which includes the PC output as an additional constraint inside the NOTEARS optimization. This approach combines PC algorithm's strengths in graph structure prediction with the NOTEARS continuous optimization to estimate causal effects accurately. PCnt achieved best aggregate performance across all structural and effect size metrics. https://github.com/zhu-yh1/PC-NOTEARS.

On the Non-identity Causal Theory of Spacetime from Causal Set Theory

On the Non-identity Causal Theory of Spacetime from Causal Set Theory

The reciprocal relationships between economic status and mental health: Investigating the between-person and within-person effects in a three-wave longitudinal study

BackgroundSocial drift/selection (i.e., mental health symptoms cause low economic status) and social causation theories (i.e., low economic status causes mental health symptoms) specify reciprocal relationships between economic status and mental health. Little is known regarding the disaggregation of within-person and between-person effects in the relationship between economic status and mental health in the long run. The current study sought to examine the reciprocal relationships between economic status and mental health over 20 years of adulthood, disaggregating within-person and between-person effects. MethodsData were from three waves (7108 participants) of the Midlife Development in the United States study. Participants reported information about objective and subjective measures of economic status as well as a wide range of indicators of mental health on the positive dimension, including subjective, social, and psychological well-being, and on the negative side, depression, anxiety, panic attack, anhedonia, somatic amplification, alcohol abuse, and negative affect. Cross-lagged panel models were estimated. ResultsAt between-person levels, both social drift/selection and social causation hypotheses were confirmed when considering subjective measures of economic status. When using objective measures of economic status (i.e., income), the results showed decreased support for social drift/selection and social causation hypotheses. At within-person levels, social drift/selection and social causation hypotheses were generally not supported, with some notable exceptions. LimitationsRestriction of the sample to one country limits the generalizability of the findings. ConclusionsSocial causation and social drift/selection processes act simultaneously mainly at a population level, but much less when considering individual changes. Policy and programs should be targeted at addressing inequality in income and mental health within a nation or a community.

Axiomatization of interventional probability distributions

Abstract Causal intervention is an essential tool in causal inference. It is axiomatized under the rules of do-calculus in the case of structure causal models. We provide simple axiomatizations for families of probability distributions to be different types of interventional distributions. Our axiomatizations neatly lead to a simple and clear theory of causality that has several advantages: it does not need to make use of any modelling assumptions such as those imposed by structural causal models; it only relies on interventions on single variables; it includes most cases with latent variables and causal cycles; and more importantly, it does not assume the existence of an underlying true causal graph as we do not take it as the primitive object; moreover, a causal graph is derived as a by-product of our theory. We show that, under our axiomatizations, the intervened distributions are Markovian to the defined intervened causal graphs, and an observed joint probability distribution is Markovian to the obtained causal graph; these results are consistent with the case of structural causal models, and as a result, the existing theory of causal inference applies. We also show that a large class of natural structural causal models satisfy the theory presented here. The aim of this paper is axiomatization of interventional families, which is subtly different from causal modelling.

A Lewisian regularity theory

In this paper, we develop a non-reductive variant of the regularity theory of causation proposed in Andreas and Günther (Pacific Philosophical Quarterly 105: 2–32, 2024). The variant is motivated as a refinement of Lewis’s (Journal of Philosophy 70:556–567, 1973) regularity theory. We do not pursue a reductive theory here because we found a challenge for Baumgartner's (Erkenntnis 78:85–109, 2013) regularity theory which applies to our previous theory as well. The challenge is sidestepped by a framework of law-like propositions resembling structural equations. We furthermore improve the deviancy condition of our previous theory. Finally, we show that the present theory can compete with the most advanced regularity and counterfactual accounts.

Infants evaluate informativeness of evidence and predict causal events as revealed in theta oscillations and predictive looking

This study investigates 16-month-old infants’ sensitivity to the informativeness of evidence and its potential link to infants’ ability to draw accurate causal inferences and predict unfolding events. Employing concurrent EEG and eye tracking, data from 66 infants revealed significantly increased theta oscillatory activity when infants expected to see causally unconfounded evidence compared to confounded evidence, suggesting heightened cognitive engagement in anticipation of informative evidence. Crucially, this difference was more pronounced in the subset of infants who later made correct predictions, suggesting that they had correctly inferred the causal structure based on the evidence presented. This research sheds light on infants’ motivation to seek explanatory causal information, suggesting that even at 16 months, infants can strategically direct attention to situations conducive to acquiring informative evidence, potentially laying the groundwork for the impressive abilities of humans to rapidly acquire knowledge and develop causal theories of the world.

Do Children with High Callous-Unemotional Traits Have Attentional Deficits to Emotional Stimuli? Evidence from a Multi-Method and Multi-Informant Study.

Callous-unemotional (CU) traits in children and adolescents are linked to severe and persistent antisocial behavior. Based on past empirical research, several theoretical models have suggested that CU traits may be partly explained by difficulties in correctly identifying others' emotional states as well as their reduced attention to others' eyes, which could be important for both causal theory and treatment. This study tested the relationships among CU traits, emotion recognition of facial expressions and visual behavior in a sample of 52 boys referred to a clinic for conduct problems (Mage = 10.29years; SD = 2.06). We conducted a multi-method and multi-informant assessment of CU traits through the Child Problematic Traits Inventory (CPTI), the Inventory of Callous-Unemotional (ICU), and the Clinical Assessment of Prosocial Emotions-Version 1.1 (CAPE). The primary goal of the study was to compare the utility of these methods for forming subgroups of youth that differ in their emotional processing abilities. An emotion recognition task assessed recognition accuracy (percentage of mistakes) and absolute dwell time on the eyes or mouth region for each emotion. Results from repeated measures ANOVAs revealed that low and high CU groups did not differ in emotion recognition accuracy, irrespective of the method of assessing CU traits. However, the high CU group showed reduced attention to the eyes of fearful and sad facial expressions (using the CPTI) or to all emotions (using the CAPE). The high CU group also showed a general increasein attention to the mouth area, but only when assessed by the CAPE. These findings provide evidence to support abnormalities in how those elevated on CU traits process emotional stimuli, especially when assessed by a clinical interview, which could guide appropriate assessment and more successful interventions for this group of youth.

Causal sets and an emerging continuum

Causal set theory offers a simple and elegant picture of discrete physics. But the vast majority of causal sets look nothing at all like continuum spacetimes, and must be excluded in some way to obtain a realistic theory. I describe recent results showing that almost all non-manifoldlike causal sets are, in fact, very strongly suppressed in the gravitational path integral. This does not quite demonstrate the emergence of a continuum—we do not yet understand the remaining unsuppressed causal sets well enough—but it is a significant step in that direction.