Journal articles: 'Causal relationship models'

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Belevich, M. "Relationship between standard and causal fluid models." Acta Mechanica 180, no. 1-4 (September 8, 2005): 83–106. http://dx.doi.org/10.1007/s00707-005-0262-y.

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Ranstam, J., and J. A. Cook. "Causal relationship and confounding in statistical models." British Journal of Surgery 103, no. 11 (September 22, 2016): 1445–46. http://dx.doi.org/10.1002/bjs.10241.

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Baetu, Tudor M. "Do Biopsychosocial Causal Models Rule Out Physicalism?" Journal of Consciousness Studies 29, no. 1 (January 15, 2022): 6–29. http://dx.doi.org/10.53765/20512201.29.1.006.

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Donald Price and James Barrell, two eminent pain researchers, argue that every time an experiment demonstrates that a biological variable is causally relevant to a psychological outcome, we are entitled to further rule out a possible mind–brain identity or supervenience relationship. As experimental evidence for two-way causal links between biological and psychological variables accumulates, more and more identity and supervenience relationships are ruled out, suggesting that psychophysicalism is empirically better supported than physicalism. I raise an objection to this line of argumentation by pointing out that, in the studies in question, causation is established within an operationalized framework; that is, irrespective of whether one knows what exactly is being manipulated and measured and how the intervention and measurement techniques work. By itself, evidence for causal relevance doesn't demonstrate that to each manipulated variable corresponds an ontologically distinct cause. This opens the possibility that some variables share common referents, as postulated by physicalist accounts. Moreover, even if it is not clear how to test for identity or supervenience relationships, it is still possible to test for causal mediation, which can generate empirical evidence discriminating between reductive physicalism and non-reductive alternatives.

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Malina, Stephen, Daniel Cizin, and David A. Knowles. "Deep mendelian randomization: Investigating the causal knowledge of genomic deep learning models." PLOS Computational Biology 18, no. 10 (October 20, 2022): e1009880. http://dx.doi.org/10.1371/journal.pcbi.1009880.

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Multi-task deep learning (DL) models can accurately predict diverse genomic marks from sequence, but whether these models learn the causal relationships between genomic marks is unknown. Here, we describe Deep Mendelian Randomization (DeepMR), a method for estimating causal relationships between genomic marks learned by genomic DL models. By combining Mendelian randomization with in silico mutagenesis, DeepMR obtains local (locus specific) and global estimates of (an assumed) linear causal relationship between marks. In a simulation designed to test recovery of pairwise causal relations between transcription factors (TFs), DeepMR gives accurate and unbiased estimates of the ‘true’ global causal effect, but its coverage decays in the presence of sequence-dependent confounding. We then apply DeepMR to examine the global relationships learned by a state-of-the-art DL model, BPNet, between TFs involved in reprogramming. DeepMR’s causal effect estimates validate previously hypothesized relationships between TFs and suggest new relationships for future investigation.

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Chvykov, Pavel, and Erik Hoel. "Causal Geometry." Entropy 23, no. 1 (December 26, 2020): 24. http://dx.doi.org/10.3390/e23010024.

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Information geometry has offered a way to formally study the efficacy of scientific models by quantifying the impact of model parameters on the predicted effects. However, there has been little formal investigation of causation in this framework, despite causal models being a fundamental part of science and explanation. Here, we introduce causal geometry, which formalizes not only how outcomes are impacted by parameters, but also how the parameters of a model can be intervened upon. Therefore, we introduce a geometric version of “effective information”—a known measure of the informativeness of a causal relationship. We show that it is given by the matching between the space of effects and the space of interventions, in the form of their geometric congruence. Therefore, given a fixed intervention capability, an effective causal model is one that is well matched to those interventions. This is a consequence of “causal emergence,” wherein macroscopic causal relationships may carry more information than “fundamental” microscopic ones. We thus argue that a coarse-grained model may, paradoxically, be more informative than the microscopic one, especially when it better matches the scale of accessible interventions—as we illustrate on toy examples.

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Haynes, Stephen N. "Causal models and the assessment-treatment relationship in behavior therapy." Journal of Psychopathology and Behavioral Assessment 10, no. 2 (June 1988): 171–83. http://dx.doi.org/10.1007/bf00962642.

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HALPERN, JOSEPH Y. "FROM CAUSAL MODELS TO COUNTERFACTUAL STRUCTURES." Review of Symbolic Logic 6, no. 2 (November 12, 2012): 305–22. http://dx.doi.org/10.1017/s1755020312000305.

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AbstractGalles & Pearl (l998) claimed that “for recursive models, the causal model framework does not add any restrictions to counterfactuals, beyond those imposed by Lewis’s [possible-worlds] framework.” This claim is examined carefully, with the goal of clarifying the exact relationship between causal models and Lewis’s framework. Recursive models are shown to correspond precisely to a subclass of (possible-world) counterfactual structures. On the other hand, a slight generalization of recursive models, models where all equations have unique solutions, is shown to be incomparable in expressive power to counterfactual structures, despite the fact that the Galles and Pearl arguments should apply to them as well. The problem with the Galles and Pearl argument is identified: an axiom that they viewed as irrelevant, because it involved disjunction (which was not in their language), is not irrelevant at all.

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Guo, Guang, and Leah K. VanWey. "Sibship Size and Intellectual Development: Is the Relationship Causal?" American Sociological Review 64, no. 2 (April 1999): 169–87. http://dx.doi.org/10.1177/000312249906400202.

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Previous research has consistently found a negative statistical relationship between sibship size and children's intellectual development. Two explanations have been offered for this finding. The prevailing explanation is that the relationship is causal, suggesting that limiting family size would lead to more intelligent children. A second explanation maintains that the relationship is spurious—that one or more undetermined factors correlated with family size are causally related to intellectual development. Using data on children from the National Longitudinal Survey of Youth, we reexamine the issue using change models. These change models allow us to control for such unmeasured effects as family intellectual climate, family value system, and family genetic heritage. We begin by replicating in these data the negative statistical relationship between three cognitive measures and sibship size. We then apply the change models to siblings measured at two points in time and to repeated measures of the same individuals. By considering sibship size as an individual trait that changes over time, we control for effects that are shared across siblings and over time. When these shared effects are controlled, the negative relationship between sibship size and intellectual development disappears, casting doubt on the causal interpretation of the negative relationship conventionally found.

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Yen, Chi-Hsien (Eric), Haocong Cheng, Grace Yu-Chun Yen, Brian P. Bailey, and Yun Huang. "Narratives + Diagrams: An Integrated Approach for Externalizing and Sharing People's Causal Beliefs." Proceedings of the ACM on Human-Computer Interaction 5, CSCW2 (October 13, 2021): 1–27. http://dx.doi.org/10.1145/3479588.

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Causal knowledge is of interest in many areas, such as statistics and machine learning, as it allows people and algorithms to predict outcomes and make data-driven decisions. Researchers in CSCW have proposed tools and workflows to externalize causal knowledge or beliefs from a group of people; however, most of the generated causal diagrams lack a deeper understanding of the causal mechanisms or could not capture diverse beliefs. By integrating narratives with causal diagrams, we implemented an interactive system that allows users to 1) write narratives to rationalize their perceived causal relationships, 2) visualize their causal models using directed diagrams, and 3) review and utilize others' causal diagrams and narratives. We conducted a user study (N=20) to learn how participants leveraged this integrated approach to externalize their perceived causal models for a given application context. Our results showed that the approach implemented in our tool enabled the externalization of users' causal beliefs (e.g., how and why a causal relationship might occur), allowed blind spots of individuals' causal reasoning to be revealed (e.g., learning new ideas from peers), and inspired their causal reasoning (e.g., revising or adding new causal relationships). We also identified the individual differences in people's causal beliefs and observed the impacts of showing others' causal models when one is building his/her causal diagram and narratives. This work provides practical design implications for developing collaborative tools that facilitate capturing and sharing causal beliefs.

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CHU, BEI-TSENG B. "DIAGNOSIS WITH CONTINUOUS AND DISCRETE CAUSAL RELATIONSHIPS: KNOWLEDGE REPRESENTATION." International Journal of Pattern Recognition and Artificial Intelligence 06, no. 04 (October 1992): 731–51. http://dx.doi.org/10.1142/s0218001492000370.

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Most present research on diagnostic reasoning has dealt with discrete causal relationships. However, continuous causal relationships are frequently encountered in many applications, particularly in equipment/instrument diagnostic applications. This paper offers an integrated model of discrete and continuous causal relationships based on probability theory. Four types of continuous and discrete causal relationships are identified. For each type of causal relationship the following issues are addressed: its semantics and causation strength based on probability theory, and how causation strength can be acquired. It is also demonstrated that causation probabilities for continuous causal relationships can be derived based on classical statistical theory and Bayesian probability theory. It will be shown in a companion paper that the knowledge representation framework presented in this paper can be used in causal network-based diagnostic inference models.

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Al-Dababi, Khaldoun, Rabe’a Al- Dababi, and Abdulsalam Abdelrahman. "Causal Relationship Modeling of the Big-Five Factors, Self-Efficacy, and Happiness of Jordan University of Science and Technology Students." Journal of Educational and Psychological Studies [JEPS] 13, no. 1 (January 31, 2019): 46–64. http://dx.doi.org/10.53543/jeps.vol13iss1pp46-64.

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The purpose of this study was to unveil the causal relationship of the big-five factors, self-efficacy and happiness for JUST students. Based on scientific foundations, it constructed a proposed causal relationship model using path analysis for interpreting happiness. To achieve this goal, the Big Five Factors by John, Donahue & Kentle, (1991), the General Self-Efficacy Scale of Schwarzer & Jerusalem, (1995), and Oxford Happiness Inventory were employed. The sample consisted of 377 students chosen on availability grounds. The results showed no statistically significant differences between the proposed and the optimal causal relationship models due to high matches on AGFI=0.942; NFI=0.986; RMSEA=0.069; GFI=0.994; IFI=0.991; TLI=0.933; and CFI=0.990. Thus, the model construes the relationships proposed and represents the optimal causal relationship model for the variables of the study.

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Al-Dababi, Khaldoun, Rabe’a Al- Dababi, and Abdulsalam Abdelrahman. "Causal Relationship Modeling of the Big-Five Factors, Self-Efficacy, and Happiness of Jordan University of Science and Technology Students." Journal of Educational and Psychological Studies [JEPS] 13, no. 1 (January 31, 2019): 46. http://dx.doi.org/10.24200/jeps.vol13iss1pp46-64.

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The purpose of this study was to unveil the causal relationship of the big-five factors, self-efficacy and happiness for JUST students. Based on scientific foundations, it constructed a proposed causal relationship model using path analysis for interpreting happiness. To achieve this goal, the Big Five Factors by John, Donahue & Kentle, (1991), the General Self-Efficacy Scale of Schwarzer & Jerusalem, (1995), and Oxford Happiness Inventory were employed. The sample consisted of 377 students chosen on availability grounds. The results showed no statistically significant differences between the proposed and the optimal causal relationship models due to high matches on AGFI=0.942; NFI=0.986; RMSEA=0.069; GFI=0.994; IFI=0.991; TLI=0.933; and CFI=0.990. Thus, the model construes the relationships proposed and represents the optimal causal relationship model for the variables of the study.

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Alanko, Katarina, Pekka Santtila, Benny Salo, Patrik Jern, Ada Johansson, and N. Kenneth Sandnabba. "Testing causal models of the relationship between childhood gender atypical behaviour and parent-child relationship." British Journal of Developmental Psychology 29, no. 2 (November 29, 2010): 214–33. http://dx.doi.org/10.1348/2044-835x.002004.

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Huang, Tung-Chun, and Wan-Jung Hsiao. "THE CAUSAL RELATIONSHIP BETWEEN JOB SATISFACTION AND ORGANIZATIONAL COMMITMENT." Social Behavior and Personality: an international journal 35, no. 9 (January 1, 2007): 1265–76. http://dx.doi.org/10.2224/sbp.2007.35.9.1265.

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Job Satisfaction (JS) and Organizational Commitment (OC) have been the subjects of a large amount of empirical research, but the nature of the causal relationship between JS and OC is still disputed. This paper provides a thorough assessment of the causal relationship between JS and OC by controlling the influence of personal traits and organizational attributes. Comparing the four models (JS ←→ OC, JS → OC, OC → JS, and no causal effect between JS and OC), our result shows that the reciprocal relation model best fits the data.

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Balabanov, O. S. "From temporal data to dynamic causal models." PROBLEMS IN PROGRAMMING, no. 3-4 (December 2022): 183–95. http://dx.doi.org/10.15407/pp2022.03-04.183.

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We present a brief review of dynamic causal model inference from data. A vector autoregressive models is of our prime interest. The architecture, representation and schemes of measurement of temporal data and time series data are outlined. We argue that require- ment to data characteristics should come from the nature of dynamic process at hand and goals of model inference. To describe and evaluate temporal data one may use terms of longitude, measurement frequency etc. Data measurement frequency is crucial factor in order to an inferred model be adequate. Data longitude and observation session duration may be expressed via several temporal horizons, such as closest horizon, 2-step horizon, influence attainability horizon, oscillatory horizon, and evolutionary horizon. To justify a dynamic causal model inference from data, analyst needs to assume the dynamic process is stationary or at least obeys structural regularity. The main specificity of task of dynamic causal model inference is known temporal order of variables and certain structural regularity. If maximal lag of influence is unknown, inference of dynamic causal model faces additional problems. We examine the Granger’s causality concept and outline its deficiency in real circumstances. It is argued that Granger causality is incorrect as practical tool of causal discovery. In contrast, certain rules of edge orientation (included in known constraint-based algorithms of model inference) can reveal unconfounded causal relationship.

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Kato, Daiki, and Mikie Suzuki. "The Causal Relationship among Rolefulness, Self-esteem, and Depression." Journal of Psychology & Behavior Research 2, no. 2 (August 10, 2020): p32. http://dx.doi.org/10.22158/jpbr.v2n2p32.

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The main purpose of this study was to examine the causal relationship between rolefulness and depression. In particular, the mediating effect of self-esteem on the relationship between rolefulness and depression was examined. Based on data from 856 Japanese high school students, three models were constructed and the validity thereof examined. The most appropriate model revealed that social rolefulness affected internal rolefulness, self-esteem, and depression. Subsequently, internal rolefulness improved self-esteem and reduced depression.

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Abdelrahman, Abdelsalam H., and Khaldoun I. Al Dbabi. "Causal Relationship Modeling of the Implicit Theories of Emotion and Emotion Regulation in View of the Cognitive Reappraisal Strategy and Happiness." Journal of Educational and Psychological Studies [JEPS] 13, no. 3 (July 11, 2019): 538–57. http://dx.doi.org/10.53543/jeps.vol13iss3pp538-557.

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The purpose of this study was to unveil the causal relationship modeling of the implicit theories of emotion and emotion regulation in view of cognitive reappraisal strategy and happiness for the students of the Jordanian University of Science and Technology (JUST). Based on scientific foundations, the study has constructed a proposed causal relationship model using path analysis for interpreting happiness. To achieve this goal, the emotion regulation, by Gross and John (2003), the implicit theories of emotion Scale of Livingstone, (2012), and the Oxford Happiness Inventory were employed. The sample consisted of 350 students who were chosen on availability grounds. The results of the study showed no statistically significant differences between the proposed and the optimal causal relationship models due to high matches on: AGFI=0.90, NFI=0.98, GFI=0.99, TLI =0.933, CFI=0.99, RMR=0.022. Thus, the model explained the relationships proposed and represented the optimal causal relationship model for the variables of the study.

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Abdelrahman, Abdelsalam H., and Khaldoun I. Al Dbabi. "Causal Relationship Modeling of the Implicit Theories of Emotion and Emotion Regulation in View of the Cognitive Reappraisal Strategy and Happiness." Journal of Educational and Psychological Studies [JEPS] 13, no. 3 (July 11, 2019): 538. http://dx.doi.org/10.24200/jeps.vol13iss3pp538-557.

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The purpose of this study was to unveil the causal relationship modeling of the implicit theories of emotion and emotion regulation in view of cognitive reappraisal strategy and happiness for the students of the Jordanian University of Science and Technology (JUST). Based on scientific foundations, the study has constructed a proposed causal relationship model using path analysis for interpreting happiness. To achieve this goal, the emotion regulation, by Gross and John (2003), the implicit theories of emotion Scale of Livingstone, (2012), and the Oxford Happiness Inventory were employed. The sample consisted of 350 students who were chosen on availability grounds. The results of the study showed no statistically significant differences between the proposed and the optimal causal relationship models due to high matches on: AGFI=0.90, NFI=0.98, GFI=0.99, TLI =0.933, CFI=0.99, RMR=0.022. Thus, the model explained the relationships proposed and represented the optimal causal relationship model for the variables of the study.

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Chen, Y. C., C. F. Kao, M. K. Lu, Y. K. Yang, S. C. Liao, F. L. Jang, W. J. Chen, R. B. Lu, and P. H. Kuo. "The relationship of family characteristics and bipolar disorder using causal-pie models." European Psychiatry 29, no. 1 (January 2014): 36–43. http://dx.doi.org/10.1016/j.eurpsy.2013.05.004.

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AbstractMany family characteristics were reported to increase the risk of bipolar disorder (BPD). The development of BPD may be mediated through different pathways, involving diverse risk factor profiles. We evaluated the associations of family characteristics to build influential causal-pie models to estimate their contributions on the risk of developing BPD at the population level. We recruited 329 clinically diagnosed BPD patients and 202 healthy controls to collect information in parental psychopathology, parent-child relationship, and conflict within family. Other than logistic regression models, we applied causal-pie models to identify pathways involved with different family factors for BPD. The risk of BPD was significantly increased with parental depression, neurosis, anxiety, paternal substance use problems, and poor relationship with parents. Having a depressed mother further predicted early onset of BPD. Additionally, a greater risk for BPD was observed with higher numbers of paternal/maternal psychopathologies. Three significant risk profiles were identified for BPD, including paternal substance use problems (73.0%), maternal depression (17.6%), and through poor relationship with parents and conflict within the family (6.3%). Our findings demonstrate that different aspects of family characteristics elicit negative impacts on bipolar illness, which can be utilized to target specific factors to design and employ efficient intervention programs.

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Alharbi, Yassir, Daniel Arribas-Bel, and Frans Coenen. "Sustainable Development Goal Relational Modelling and Prediction." Journal of Data Intelligence 2, no. 3 (September 2021): 348–67. http://dx.doi.org/10.26421/jdi2.3-3.

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A methodology for UN Sustainable Development Goal (SDG) attainment prediction is presented, the Sustainable Development Goals Correlation Attainment Predictions Extended framework SDG-CAP-EXT. Unlike previous SDG attainment methodologies, SDG-CAP-EXT takes into account the potential for a causal relationship between SDG indicators both with respect to the geographic entity under consideration (intra-entity) and neighbouring geographic entities to the current entity (inter-entity). The challenge is in the discovery of such causal relationships. A ensemble approach is presented that combines the results of a number of alternative causality relationship identification mechanisms. The identified relationships are used to build multi-variate time series prediction models that feed into a bottom-up SDG prediction taxonomy, which is used to make SDG attainment predictions and rank countries using a proposed Attainment Likelihood Index that reflects the likelihood of goal attainment. The framework is fully described and evaluated. The evaluation demonstrates that the SDG-CAP-EXT framework can produce better predictions than alternative models that do not consider the potential for intra- and inter-causal relationships.

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Hidalgo-Contreras, Juan Valente, Josafhat Salinas-Ruiz, Kent M. Eskridge, and Stephen P. Baenziger. "Incorporating Molecular Markers and Causal Structure among Traits Using a Smith-Hazel Index and Structural Equation Models." Agronomy 11, no. 10 (September 28, 2021): 1953. http://dx.doi.org/10.3390/agronomy11101953.

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The goal in breeding programs is to choose candidates that produce offspring with the best phenotypes. In conventional selection, the best candidate is selected with high genotypic values (unobserved), in the assumption that this is related to the observed phenotypic values for several traits. Multi-trait selection indices are used to identify superior genotypes when a number of traits are to be considered simultaneously. Often, the causal relationship among the traits is well known. Structural equation models (SEM) have been used to describe the causal relationships among variables in many biological systems. We present a method for multi-trait genomic selection that incorporates causal relationships among traits by coupling SEM with a Smith–Hazel index that incorporates markers. The method was applied to field data from a Nebraska winter wheat breeding program. We found that the correlation and the relative efficiency increased for the proposed Smith–Hazel indices when the total causal information among traits was accounted for by the vector of weights (b), which includes the causal path coefficients in the causal matrix (Λ). On the other hand, when selection was based on a primary trait, for example yield, the proposed SI increased the mean yield of the best 28 (Top 10%) genotypes to 7%.

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Elbedour, Annisa, Xiaoqian Cheng, Saravana R. K. Murthy, Taisen Zhuang, Lawan Ly, Olivia Jones, Giacomo Basadonna, Michael Keidar, and Jerome Canady. "The Granger Causal Effects of Canady Helios Cold Plasma on the Inhibition of Breast Cancer Cell Proliferation." Applied Sciences 12, no. 9 (May 5, 2022): 4622. http://dx.doi.org/10.3390/app12094622.

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Cold atmospheric plasma (CAP) has become a promising tool for modern medicine. With its recent applications in oncology, regenerative medicine, and immunotherapy, CAP can be used for a myriad of different clinical treatments. When using CAP specifically for the treatment of tumors, it is known to elicit an oxidative response within malignant cancer cells, inducing cell cycle arrest and apoptosis. In this study, data of intracellular reactive oxygen species (ROS), caspase activity, Ki-67 expression, and cell cycle activity in the G1 phase were acquired to determine the causal relationships these intermediates have with cell proliferation and death after Canady Helios Cold Plasma (CHCP) treatment. The data were derived from four different subtypes of breast cancer cell lines: BT-474, MCF-7, MDA-MB-231, and SK-BR-3. Data transformation techniques were conducted on the time-series data for the input into the causal model code. The models were created on the basis of Granger causality principles. Our results demonstrated that there was a Granger causal relationship among all potentially causal variables (ROS, caspase, Ki-67, and G1 activity) and cell proliferation after 5 min CHCP treatment; however, not all variables were causal for the 3 min models. This same pattern did not exist for cell death models, which tested all potentially causal variables (ROS, Ki-67, and G1 activity) vs. caspase activity. All models were validated through a variety of statistical tests and forecasting accuracy metrics. A pseudo data set with defined causal links was also created to test R’s ability in picking up known causal relationships. These models, while nonexhaustive, elucidated the effects cold plasma has on cell activity regulators. Research in causal modeling is needed to help verify the exact mechanism of cold plasma for the ultimate optimization of its application in the treatment of cancers.

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Kim, Byoung Joon, Seoyong Kim, and Sunhee Kim. "Searching for New Directions for Energy Policy: Testing Three Causal Models of Risk Perception, Attitude, and Behavior in Nuclear Energy Context." International Journal of Environmental Research and Public Health 17, no. 20 (October 12, 2020): 7403. http://dx.doi.org/10.3390/ijerph17207403.

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Although many risk studies investigate perceptions, attitudes, and behaviors, the causal relationships among them have not yet been verified. Thus, further investigations of these relationships are necessary. This study analyzes three causal models consisting of three components: perceptions (i.e., perceived risk in this study), attitudes (i.e., satisfaction), and behavior (i.e., support for policy). This study checks these relationships in the context of nuclear energy policy. Using a hierarchical regression model, this study tests three different models between the three components: (1) Model 1 (a high-involvement model), (2) Model 2 (a low-involvement model), and (3) Model 3 (a hedonic model). First, in the high-involvement model, behavior is affected by perceptions and attitudes. In particular, attitudes mediate the relationship between risk perceptions and satisfaction. Second, in the low-involvement model, attitudes indirectly affect perceptions through behaviors. Third, in the hedonic model, behaviors affect attitudes, and risk perceptions do not mediate that relationship. This causal model does not depend on perceptions of the benefits and drawbacks of nuclear power. Our analysis shows that Model 1 is fully significant, and Model 2 and 3 are only partially significant.

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Li, De-Wei, and Bryce Kendrick. "Functional and causal relationships between indoor and outdoor airborne fungi." Canadian Journal of Botany 74, no. 2 (February 1, 1996): 194–209. http://dx.doi.org/10.1139/b96-024.

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From May to October, relationships of total numbers of airborne fungal propagules between indoor and outdoor sampling sites were very strong, particularly for Alternaria and Leptosphaeria, while that for unidentified ascospores was positive but to a lesser degree. Indoor and outdoor counts of Cladosporium, Epicoccum, Ganoderma, unidentified spores, hyphal fragments, and biodiversity (total number of fungal genera) were also significantly positively related. There appeared to be no functional relationship between Aspergillus/Penicillium conidia in indoor and outdoor air. From November to April, indoor and outdoor counts of Alternaria, Ganoderma, and hyphal fragments displayed negative relationships, but there was a positive correlation for Cladosporium, Epicoccum, Leptosphaeria, unidentified ascospores, total fungal spores, unidentified spores, and biodiversity. Once again, no functional relationship was detected between Aspergillus/Penicillium indoors and outdoors. The functional relationships of airborne fungi with indoor environmental factors are examined and discussed. A lack of causal relationships, as detected by path analysis, indicates that airborne spores of Alternaria, Leptosphaeria, unidentified ascospores, Coprinus, and Ganoderma came mainly from outdoor sources. All path models fitted this hypothesis well, except for Aspergillus/Penicillium. On the other hand, path analysis suggested that there were probably indoor sources of Cladosporium, Epicoccum, Aspergillus/Penicillium, unidentified basidiospores, and unidentified spores. Most of the models explained a large proportion of variance of indoor airborne fungi. Keywords: airborne fungal spores, redundancy analysis, path analysis.

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Leenheer, Stefan, Maurice Gesthuizen, and Michael Savelkoul. "Two-Way, One-Way or Dead-End Streets? Financial and Social Causes and Consequences of Generalized Trust." Social Indicators Research 155, no. 3 (February 8, 2021): 915–37. http://dx.doi.org/10.1007/s11205-020-02591-6.

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AbstractScholars disagree on whether and to what extent adult life experiences can influence generalized trust and vice versa. Going beyond the methodological limitations of former studies, we aimed to answer the question as to what extent reciprocal causal relationships exist between generalized trust and the adult life experiences of financial success and (in)formal social contacts. We used two-wave cross-lagged panel models to identify those reciprocal causal relationships, and fixed-effects models to assess if they might be biased due to unaccounted time-invariant influences. Data from the Dutch NELLS panel study (age range 17–49) show that compelling empirical evidence is found for a reciprocal causal relationship between generalized trust and household income that does not suffer from bias due to unobserved heterogeneity. Furthermore, more trusting individuals experience a stronger decrease in material deprivation, but not vice versa. Trust and (in)formal social contacts are not causally related in any of our models.

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Lazic, Stanley E. "Using causal models to distinguish between neurogenesis-dependent and -independent effects on behaviour." Journal of The Royal Society Interface 9, no. 70 (September 28, 2011): 907–17. http://dx.doi.org/10.1098/rsif.2011.0510.

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There has been a substantial amount of research on the relationship between hippocampal neurogenesis and behaviour over the past 15 years, but the causal role that new neurons have on cognitive and affective behavioural tasks is still far from clear. This is partly due to the difficulty of manipulating levels of neurogenesis without inducing off-target effects, which might also influence behaviour. In addition, the analytical methods typically used do not directly test whether neurogenesis mediates the effect of an intervention on behaviour. Previous studies may have incorrectly attributed changes in behavioural performance to neurogenesis because the role of known (or unknown) neurogenesis-independent mechanisms was not formally taken into consideration during the analysis. Causal models can tease apart complex causal relationships and were used to demonstrate that the effect of exercise on pattern separation is via neurogenesis-independent mechanisms. Many studies in the neurogenesis literature would benefit from the use of statistical methods that can separate neurogenesis-dependent from neurogenesis-independent effects on behaviour.

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Shambharkar, Saroj, K. Vaishali, Rachna Somkunwar, Yogeshri Choudhari, and Jyotsna Gawai. "Biological Network, Gene Regulatory Network Inference Using Causal Inference Approach." Revue d'Intelligence Artificielle 36, no. 1 (February 28, 2022): 139–45. http://dx.doi.org/10.18280/ria.360116.

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In system biology inference from gene regulatory network (GRN) is a challenging task. There exist different computational techniques to analyze the causal relationships between the pair of genes and to understand the significance of causal relationship in gene regulatory network. The DREAM4 insilico network structure and insilico gene expression time series dataset of DREAM challenge dataset is examined. This gene expression dataset of insilico of size 10 is analyzed for inferring causal relationships of the GRN inference. The analysis of dataset showing the gene expression data values are varying with respect to time. The paper focused on the different models of causal inference approach, Genetic Algorithm framework for the GRN inference. In this dataset, values associated with genes are analyzed using the Granger causality test and clustering to analyze the correlation and interaction or causal relationships among genes. The objective behind analysis and inferring causal information in the GRN is to reveal the study on gene activities to achieve more biological insights.

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Rahmadi, Ridho, Perry Groot, Marieke HC van Rijn, Jan AJG van den Brand, Marianne Heins, Hans Knoop, and Tom Heskes. "Causality on longitudinal data: Stable specification search in constrained structural equation modeling." Statistical Methods in Medical Research 27, no. 12 (June 28, 2017): 3814–34. http://dx.doi.org/10.1177/0962280217713347.

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A typical problem in causal modeling is the instability of model structure learning, i.e., small changes in finite data can result in completely different optimal models. The present work introduces a novel causal modeling algorithm for longitudinal data, that is robust for finite samples based on recent advances in stability selection using subsampling and selection algorithms. Our approach uses exploratory search but allows incorporation of prior knowledge, e.g., the absence of a particular causal relationship between two specific variables. We represent causal relationships using structural equation models. Models are scored along two objectives: the model fit and the model complexity. Since both objectives are often conflicting, we apply a multi-objective evolutionary algorithm to search for Pareto optimal models. To handle the instability of small finite data samples, we repeatedly subsample the data and select those substructures (from the optimal models) that are both stable and parsimonious. These substructures can be visualized through a causal graph. Our more exploratory approach achieves at least comparable performance as, but often a significant improvement over state-of-the-art alternative approaches on a simulated data set with a known ground truth. We also present the results of our method on three real-world longitudinal data sets on chronic fatigue syndrome, Alzheimer disease, and chronic kidney disease. The findings obtained with our approach are generally in line with results from more hypothesis-driven analyses in earlier studies and suggest some novel relationships that deserve further research.

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Ikhwan, Radha, and Ariusni Ariusni. "ANALISIS KAUSALITAS INVESTASI ASING LANGSUNG (FDI), EKSPOR DAN PERTUMBUHAN EKONOMI DI INDONESIA." Jurnal Kajian Ekonomi dan Pembangunan 1, no. 2 (July 9, 2019): 383. http://dx.doi.org/10.24036/jkep.v1i2.6180.

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This study examines the causal relationship between foreign direct investment (FDI), exports and economic growth in Indonesia by using Vector Error Corroction Estimates (VECM) time series models during the period 1982 - 2017. The results show that (1)There is no causal relationship between economic growth and investment foreign direct investment (FDI), there is a one-way relationship between foreign direct investment (FDI) and economic growth, where only foreign direct investment (FDI) affects economic growth. (2)There is no causal relationship between economic growth and exports, where there is no influence between the two. (3)There is no causal relationship between foreign direct investment (FDI) and exports, where there is a one-way relationship between foreign direct investment (FDI) and exports, where only foreign direct investment (FDI) affects exports.

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Rohrer, Julia M., and Kou Murayama. "These Are Not the Effects You Are Looking for: Causality and the Within-/Between-Persons Distinction in Longitudinal Data Analysis." Advances in Methods and Practices in Psychological Science 6, no. 1 (January 2023): 251524592211408. http://dx.doi.org/10.1177/25152459221140842.

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In psychological science, researchers often pay particular attention to the distinction between within- and between-persons relationships in longitudinal data analysis. Here, we aim to clarify the relationship between the within- and between-persons distinction and causal inference and show that the distinction is informative but does not play a decisive role in causal inference. Our main points are threefold. First, within-persons data are not necessary for causal inference; for example, between-persons experiments can inform about (average) causal effects. Second, within-persons data are not sufficient for causal inference; for example, time-varying confounders can lead to spurious within-persons associations. Finally, despite not being sufficient, within-persons data can be tremendously helpful for causal inference. We provide pointers to help readers navigate the more technical literature on longitudinal models and conclude with a call for more conceptual clarity: Instead of letting statistical models dictate which substantive questions researchers ask, researchers should start with well-defined theoretical estimands, which in turn determine both study design and data analysis.

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Braumoeller, Bear F., Giampiero Marra, Rosalba Radice, and Aisha E. Bradshaw. "Flexible Causal Inference for Political Science." Political Analysis 26, no. 1 (January 2018): 54–71. http://dx.doi.org/10.1017/pan.2017.29.

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Measuring the causal impact of state behavior on outcomes is one of the biggest methodological challenges in the field of political science, for two reasons: behavior is generally endogenous, and the threat of unobserved variables that confound the relationship between behavior and outcomes is pervasive. Matching methods, widely considered to be the state of the art in causal inference in political science, are generally ill-suited to inference in the presence of unobserved confounders. Heckman-style multiple-equation models offer a solution to this problem; however, they rely on functional-form assumptions that can produce substantial bias in estimates of average treatment effects. We describe a category of models, flexible joint likelihood models, that account for both features of the data while avoiding reliance on rigid functional-form assumptions. We then assess these models’ performance in a series of neutral simulations, in which they produce substantial (55% to ${>}$90%) reduction in bias relative to competing models. Finally, we demonstrate their utility in a reanalysis of Simmons’ (2000) classic study of the impact of Article VIII commitment on compliance with the IMF’s currency-restriction regime.

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Akkasi, Abbas, and Mari-Francine Moens. "Causal relationship extraction from biomedical text using deep neural models: A comprehensive survey." Journal of Biomedical Informatics 119 (July 2021): 103820. http://dx.doi.org/10.1016/j.jbi.2021.103820.

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Ford, Lori Allen. "Selection issues of formative models." Journal of Management Development 36, no. 5 (June 12, 2017): 660–70. http://dx.doi.org/10.1108/jmd-04-2015-0057.

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Purpose The purpose of this paper is to provide an understanding of the issues related to formative modeling and the implications for its use, including some practical suggestions on methods for utilizing causal indicators within models. Design/methodology/approach In order to understand and explore the issues relating to formative models, this paper explores both formative and reflective indicators that together address the basic approach to the causal relationship within measurement models in structural equations modeling. Process recommendations for formative model/variable conceptualization and development are presented as well as a discussion of several evaluative issues including identification and proportionality. Some of the main arguments for and against its use are also discussed. Findings While the literature on formative models stresses its contribution to behavioral sciences, perhaps its greatest contribution is the flexibility of choice that it provides researchers in the development of models to more accurately estimate the relationship under consideration. Originality/value The paper contributes to the on-going debate on the relative advantages and disadvantages of the formative and reflective approaches to modeling and examination. Although the pendulum has swung in recent years away from an almost singular focus on reflective models, both approaches provide useful and complementary tools to theoretical management research modeling.

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Zhang, Aiqing, Peilan Guan, Fanglian Zhou, and Qian Lu. "MODELS OF JUDGMENTS OF BEHAVIOR RESPONSIBILITY IN CHINESE CULTURE FROM AN ATTRIBUTIONAL PERSPECTIVE." Social Behavior and Personality: an international journal 31, no. 2 (January 1, 2003): 205–13. http://dx.doi.org/10.2224/sbp.2003.31.2.205.

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Participants in this study were 217 employees and managers. Two structural equation models that reflected the relationships among locus, stability, controllability, affect responses, the change of expectancy and judgments of responsibility were set up. EQS (Bentler, 2000) was used to test the models. The authors found that their models were well supported by the data. In these models, “cognition” (judgment of responsibility) and “affect” (sympathy and anger) had a two-way directional relationship. Not only were causal locus and stability important attributional dimensions that could contribute to the judgment of responsibility, but also affect response contributed to the judgment of responsibility. Causal attribution (including locus, controllability, stability), affect and expectancy change could serve as the antecedents of judgments of behavior responsibility. These findings have important significance for our understanding of people's social behavior.

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Dritsaki, Melina, and Chaido Dritsaki. "Long-Run Stability of Money Demand and Monetary Policy: The Case of South Korea." Asian Economic and Financial Review 12, no. 5 (April 27, 2022): 296–316. http://dx.doi.org/10.55493/5002.v12i5.4482.

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The current paper aims to investigate the stability of money demand in the case of Korea. Since the economic reforms in Korea faced considerable structural changes, it was difficult to formulate a stable money demand function. The use of unit root and cointegration tests with structural breaks suggest that economic and financial deregulations have influenced the stability of money demand. The cointegration results suggest a cointegration vector in all models, implying a long-run relationship. Furthermore, the estimated long-run equations show that the elasticity of long-run industrial production to real money is positive and very close to one. Finally, the exogeneity test shows a long-run, one-way, causal relationship from industrial production to M1 and a bi-directional causality between M1 and interest rate in both models. In the case of M3, there are long-run, one-way, causal relationships from industrial production and interest rate to M3 in both models.

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Garrido, Sergio, Stanislav Borysov, Jeppe Rich, and Francisco Pereira. "Estimating causal effects with the neural autoregressive density estimator." Journal of Causal Inference 9, no. 1 (January 1, 2021): 211–28. http://dx.doi.org/10.1515/jci-2020-0007.

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Abstract The estimation of causal effects is fundamental in situations where the underlying system will be subject to active interventions. Part of building a causal inference engine is defining how variables relate to each other, that is, defining the functional relationship between variables entailed by the graph conditional dependencies. In this article, we deviate from the common assumption of linear relationships in causal models by making use of neural autoregressive density estimators and use them to estimate causal effects within Pearl’s do-calculus framework. Using synthetic data, we show that the approach can retrieve causal effects from non-linear systems without explicitly modeling the interactions between the variables and include confidence bands using the non-parametric bootstrap. We also explore scenarios that deviate from the ideal causal effect estimation setting such as poor data support or unobserved confounders.

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Machlus, Kellie R., Jessica C. Cardenas, Frank C. Church, and Alisa S. Wolberg. "Causal relationship between hyperfibrinogenemia, thrombosis, and resistance to thrombolysis in mice." Blood 117, no. 18 (May 5, 2011): 4953–63. http://dx.doi.org/10.1182/blood-2010-11-316885.

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AbstractEpidemiologic studies have correlated elevated plasma fibrinogen (hyperfibrinogenemia) with risk of cardiovascular disease and arterial and venous thrombosis. However, it is unknown whether hyperfibrinogenemia is merely a biomarker of the proinflammatory disease state or is a causative mechanism in the etiology. We raised plasma fibrinogen levels in mice via intravenous infusion and induced thrombosis by ferric chloride application to the carotid artery (high shear) or saphenous vein (lower shear); hyperfibrinogenemia significantly shortened the time to occlusion in both models. Using immunohistochemistry, turbidity, confocal microscopy, and elastometry of clots produced in cell and tissue factor-initiated models of thrombosis, we show that hyperfibrinogenemia increased thrombus fibrin content, promoted faster fibrin formation, and increased fibrin network density, strength, and stability. Hyperfibrinogenemia also increased thrombus resistance to tenecteplase-induced thrombolysis in vivo. These data indicate that hyperfibrinogenemia directly promotes thrombosis and thrombolysis resistance and does so via enhanced fibrin formation and stability. These findings strongly suggest a causative role for hyperfibrinogenemia in acute thrombosis and have significant implications for thrombolytic therapy. Plasma fibrinogen levels may be used to identify patients at risk for thrombosis and inform thrombolytic administration for treating acute thrombosis/thromboembolism.

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Burkhart, Ross. "Democracy, Capitalism, and Income Inequality: Seeking Causal Directions." Comparative Sociology 6, no. 4 (2007): 481–507. http://dx.doi.org/10.1163/156913307x233683.

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AbstractRecent research shows that lower levels of income inequality cause higher levels of democracy, and vice versa in a simultaneous relationship. A critical factor missing from these studies is a direct exogenous measure of capitalism in models explaining variation in income inequality and democracy. This study examines 50 countries over the years 1978–1993 and finds in a pooled two stage least squares modeling exercise that the Fraser Institute measure of capitalism appears to have a weakly positive linear impact on POLITY IV measures of democracy and a weakly positive linear impact on income inequality (more capitalism, more inequality). There appears to be no higher-order relationship between capitalism and democracy or income inequality, though there is a weak parabolic relationship between democracy and income inequality.

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Barons, Martine Jayne, and Rachel L. Wilkerson. "Proof and Uncertainty in Causal Claims." Exchanges: The Interdisciplinary Research Journal 5, no. 2 (June 7, 2018): 72–89. http://dx.doi.org/10.31273/eirj.v5i2.238.

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Causal questions drive scientific enquiry. From Hume to Granger, and Rubin to Pearl the history of science is full of examples of scientists testing new theories in an effort to uncover causal mechanisms. The difficulty of drawing causal conclusions from observational data has prompted developments in new methodologies, most notably in the area of graphical models. We explore the relationship between existing theories about causal mechanisms in a social science domain, new mathematical and statistical modelling methods, the role of mathematical proof and the importance of accounting for uncertainty. We show that, while the mathematical sciences rely on their modelling assumptions, dialogue with the social sciences calls for continual extension of these models. We show how changing model assumptions lead to innovative causal structures and more nuanced casual explanations. We review differing techniques for determining cause in different disciplines using causal theories from psychology, medicine, and economics.

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Yan, Fengxiao, Bo Shen, and Chenyang Dai. "Causality Extraction Cascade Model Based on Dual Labeling." Journal of Advanced Computational Intelligence and Intelligent Informatics 27, no. 3 (May 20, 2023): 421–30. http://dx.doi.org/10.20965/jaciii.2023.p0421.

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Causal relation extraction is a crucial task in natural language processing. Current extraction methods have problems, including low accuracy of causal-event division and incorrect extraction of important semantic features. This study uses the bidirectional long short-term memory (BiLSTM) and attentive convolutional neural network (ACNN) models to construct a cascaded causal relationship extraction model to improve the precision of the extraction. The model uses two kinds of labels and then divides the causal event boundary after determining the relationship between the front and rear causal events. It automatically learns semantic features from sentences, reducing the dependence on external knowledge and improving the precision of extraction. The experimental results demonstrate that the precision of causality extraction can reach 81.67% and the F1 value can reach 83.2%.

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Gil-Saura, Irene, Maja Šerić, María Eugenia Ruiz-Molina, and Gloria Berenguer-Contrí. "The causal relationship between store equity and loyalty: Testing two alternative models in retailing." Journal of Brand Management 24, no. 2 (February 16, 2017): 193–208. http://dx.doi.org/10.1057/s41262-016-0024-2.

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García, Jose Antonio Martínez, and Laura Martínez Caro. "Forum: Building Better Causal Models to Measure the Relationship between Attitudes and Customer Loyalty." International Journal of Market Research 50, no. 4 (July 2008): 437–47. http://dx.doi.org/10.1177/147078530805000404.

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Heinrich, Horst-Alfred. "Causal Relationship or Not? Nationalism, Patriotism, and Anti-immigration Attitudes in Germany." Sociology of Race and Ethnicity 6, no. 1 (August 2, 2018): 76–91. http://dx.doi.org/10.1177/2332649218788583.

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Despite broad research on the connection between in-group and out-group attitudes, empirical studies dealing with the relationship between nation-related and anti-immigration attitudes rarely provide a consistent theoretical framework. On one hand, it is assumed that if persons agree with nationalistic statements, they might develop an orientation against strangers. On the other hand, one might imagine the existence of simple factor correlations among nationalism, patriotism, and anti-immigration attitudes. It can be argued that if people form a group, they will be automatically confronted with out-group members. Both proposals can claim some plausibility. But as several empirical studies mirror varying theoretical assumptions, the author compares different structure models on the basis of German International Social Survey Programme data. Two models lead to satisfactory solutions. Their respective theoretical meaning is discussed in detail. As a result, personal construct theory is integrated here as a theoretical framework with which to explain the correlational structure of a model with three factors without assuming any causality between them.

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Chung, Heeyoung, Jeongjun Park, Byung-Kyu Kim, Kibeom Kwon, In-Mo Lee, and Hangseok Choi. "A Causal Network-Based Risk Matrix Model Applicable to Shield TBM Tunneling Projects." Sustainability 13, no. 9 (April 26, 2021): 4846. http://dx.doi.org/10.3390/su13094846.

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The present study compares and analyzes three risk analysis models that are applicable to shield tunnel boring machine (TBM) tunneling, and thus proposes an improved risk matrix model based on the causal networks applicable to sustainable tunnel projects. The advantages and disadvantages of three risk analysis models are compared, and causal networks are structured by analyzing the causal relationship between risk factors and risk events. Based on the comparison and analysis results, the causal network-based risk matrix model (CN-Matrix model), which complements the disadvantages and exploits the advantages of the three existing models, is proposed in this paper. Furthermore, this study suggests a means of modifying the weighting scores in the estimation of the risk score, which permits the CN-Matrix model to determine the risk level more reasonably. Thus, the improved CN-Matrix model is more reliable and robust compared to the three existing models.

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Sapiri, Hasimah, Nazihah Ahmad, Nazrina Aziz, and Fatinah Zainon. "Using Causal Loop Diagram in Understanding Financial Activities of Malaysia Pension Fund." GATR Global Journal of Business Social Sciences Review 3, no. 3 (August 19, 2013): 35–40. http://dx.doi.org/10.35609/gjbssr.2013.1.3(5).

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Objective - The aim of the article is to present the development of a simulation framework which identifies Malaysia Pension Fund main sources as well as the financial activities. Methodology/Technique - Numerous factors influencing the financial condition pension fund are summarized. Based on the set of system factors in literature summarization, causal loop diagram is used to analyse the inter-relationships among these factors. Findings - Causal loop diagram shows a relationship between these factors, which can be used in analysing financial condition of a pension fund. In the developed causal loop diagram, it shows the main sources of Malaysia pension fund, the asset allocation, and the future benefit payment. Novelty - The causal loop diagram will then, be used as a dynamic hypothesis of this study and serves as a basis for quantitative models. Type of Paper: Review Keywords: ,Pension Fund, Financial Activities, Simulation, Causal Loop Diagram.

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Drury, Brett, Hugo Gonçalo Oliveira, and Alneu de Andrade Lopes. "A survey of the extraction and applications of causal relations." Natural Language Engineering 28, no. 3 (January 20, 2022): 361–400. http://dx.doi.org/10.1017/s135132492100036x.

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AbstractCausationin written natural language can express a strong relationship between events and facts. Causation in the written form can be referred to as a causal relation where a cause event entails the occurrence of an effect event. A cause and effect relationship is stronger than a correlation between events, and therefore aggregated causal relations extracted from large corpora can be used in numerous applications such as question-answering and summarisation to produce superior results than traditional approaches. Techniques like logical consequence allow causal relations to be used in niche practical applications such as event prediction which is useful for diverse domains such as security and finance. Until recently, the use of causal relations was a relatively unpopular technique because the causal relation extraction techniques were problematic, and the relations returned were incomplete, error prone or simplistic. The recent adoption of language models and improved relation extractors for natural language such as Transformer-XL (Dai et al. (2019). Transformer-xl: Attentive language models beyond a fixed-length context. arXiv preprint arXiv:1901.02860) has seen a surge of research interest in the possibilities of using causal relations in practical applications. Until now, there has not been an extensive survey of the practical applications of causal relations; therefore, this survey is intended precisely to demonstrate the potential of causal relations. It is a comprehensive survey of the work on the extraction of causal relations and their applications, while also discussing the nature of causation and its representation in text.

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Milo, Ron, Amos D. Korczyn, Navid Manouchehri, and Olaf Stüve. "The temporal and causal relationship between inflammation and neurodegeneration in multiple sclerosis." Multiple Sclerosis Journal 26, no. 8 (November 4, 2019): 876–86. http://dx.doi.org/10.1177/1352458519886943.

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It is currently incompletely understood whether inflammation and neurodegeneration are causally related in multiple sclerosis (MS). The sequence of a potential causal relationship is also unknown. Inflammation is present in rather all clinical stages of MS. Its role in the pathogenesis of MS is supported by histopathological analyses, genetic data, and numerous animal models of MS. All approved disease-modifying therapies that reduce clinical relapses and diminish the accumulation of lesions on neuroimaging are anti-inflammatory. Axonal loss and accelerated brain volume loss can also be detected from clinical disease onset throughout all stages. The expression of neurofilament light chain in cerebrospinal fluid and serum, a scaffolding protein in axons and dendrites, is a biomarker of neuronal injury associated with clinical relapses and reflects neuronal loss during episodes of acute inflammation. The recent association of human endogenous retrovirus (HERV) and its envelope proteins with MS illustrates a pathogenic pathway that causally links central nervous system (CNS)–intrinsic proinflammatory effects and inhibition of myelin repair and neuroregeneration. A review of current data on the causal relationship between inflammation and neurodegeneration in MS identified numerous plausible pathomechanisms that link the two events. Observations from most experimental models appear to favor a pathogenesis in which inflammation precedes neurodegeneration.

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Liu, Shuo Shuo, and Yeying Zhu. "Simultaneous Maximum Likelihood Estimation for Piecewise Linear Instrumental Variable Models." Entropy 24, no. 9 (September 2, 2022): 1235. http://dx.doi.org/10.3390/e24091235.

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Analysis of instrumental variables is an effective approach to dealing with endogenous variables and unmeasured confounding issue in causal inference. We propose using the piecewise linear model to fit the relationship between the continuous instrumental variable and the continuous explanatory variable, as well as the relationship between the continuous explanatory variable and the outcome variable, which generalizes the traditional linear instrumental variable models. The two-stage least square and limited information maximum likelihood methods are used for the simultaneous estimation of the regression coefficients and the threshold parameters. Furthermore, we study the limiting distribution of the estimators in the correctly specified and misspecified models and provide a robust estimation of the variance-covariance matrix. We illustrate the finite sample properties of the estimation in terms of the Monte Carlo biases, standard errors, and coverage probabilities via the simulated data. Our proposed model is applied to an education-salary data, which investigates the causal effect of children’s years of schooling on estimated hourly wage with father’s years of schooling as the instrumental variable.

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Pogozhina, I. N. "The Models of Relationship between Training and Psyche development in Cultural-historical and Activity Approaches." Psychological-Educational Studies 8, no. 3 (2016): 16–31. http://dx.doi.org/10.17759/psyedu.2016080302.

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The possibility of referring of the psychological theories studying interrelation of training and mental development processes to this or that stage of scientific knowledge formation on the basis of studied objects types and corresponded determination systems as a basic criterion distinguishing the ideals of scientific rationality is justified. General characteristics of classical, non-classical and post-non-classical models, determination of the mechanisms of dissipative systems, requirements for learning and development model building in the context of post-non-classic science paradigm on the criterion of the system features of the object of cognition are described. Domestic psychological school models are compared with associanism, behaviorism, gestalt psychology and Piaget determination models on the number of options allocated to these determinants, types of causal chains and types of links between causal chains. It is shown that cultural-historical approach is situated intermediately between post-non-classical and non-classical models, while activity approach corresponds to post-non-classical understanding of the object of study as complicated self-developing "man-size" system. Determination relationships models developed by L.V.Vygotskii, S.L. Rubinstein, A.N. Leont’ev continue to play the heuristic role at the present stage of scientific development.

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Khan, Shahbaz, Sadia Samar Ali, and Rubee Singh. "Determinants of Remanufacturing Adoption for Circular Economy: A Causal Relationship Evaluation Framework." Applied System Innovation 5, no. 4 (June 24, 2022): 62. http://dx.doi.org/10.3390/asi5040062.

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Organizations are transforming their linear models into circular models in order to become more sustainable. Remanufacturing is an essential element of the circular model; thus, there is an urgent need to adopt remanufacturing. It can offer organizations economic and environmental advantages and facilitate the transition to a circular economy (CE). Several aspects are crucial to the use of remanufacturing methods in order to transition to the CE. Therefore, in this study, we aimed to develop a framework for investigating the causal relationship among determinants of adopting remanufacturing processes for the circular economy. Through an integrated approach comprising a literature review and the Modified Delphi Method, we identified ten remanufacturing adoption determinants. The causal relationship among these determinants was established using the DEMATEL method. Furthermore, we classified these determinants into cause and effect groups. Five determinants, “consumer preferences”, “remanufacturing adoption framework”, “market opportunities”, “management commitment”, and “preferential tax policies”, belong to the cause group, and the remaining five belong to the effect group based on the effect score. To implement remanufacturing processes and transition to a circular economy, it is necessary to pay greater attention to these identified determinants, especially those that belong to the cause group. The outcomes of this study may aid management and policy makers in formulating strategies for effectively implementing remanufacturing methods within their organizations.

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Journal articles on the topic 'Causal relationship models'

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