Search results for: causal map.

Authors: Tharini N. de Silva, Xiao Zhibo, Zhao Rui, Mao Kezhi

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Causal relation identification is a crucial task in information extraction and knowledge discovery. In this work, we present two approaches to causal relation identification. The first is a classification model trained on a set of knowledge-based features. The second is a deep learning based approach training a model using convolutional neural networks to classify causal relations. We experiment with several different convolutional neural networks (CNN) models based on previous work on relation extraction as well as our own research. Our models are able to identify both explicit and implicit causal relations as well as the direction of the causal relation. The results of our experiments show a higher accuracy than previously achieved for causal relation identification tasks.

Keywords: causal realtion extraction, relation extracton, convolutional neural network, text representation

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Authors: Yaowei Hu, Yongkai Wu, Lu Zhang, Xintao Wu

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Causal inference from observational data is receiving wide applications in many fields. However, unidentifiable situations, where causal effects cannot be uniquely computed from observational data, pose critical barriers to applying causal inference to complicated real applications. In this paper, we develop a bounding method for estimating the average causal effect (ACE) under unidentifiable situations due to hidden confounders. We propose to parameterize the unknown exogenous random variables and structural equations of a causal model using neural networks and implicit generative models. Then, with an adversarial learning framework, we search the parameter space to explicitly traverse causal models that agree with the given observational distribution and find those that minimize or maximize the ACE to obtain its lower and upper bounds. The proposed method does not make any assumption about the data generating process and the type of the variables. Experiments using both synthetic and real-world datasets show the effectiveness of the method.

Keywords: average causal effect, hidden confounding, bound estimation, generative adversarial learning

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Authors: Mohamed Raouf Benmakrelouf, Wafa Karouche, Joseph Rynkiewicz

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In this paper, we propose an alternative method to construct a Bayesian Network (BN). This method relies on a convolutional neural network (CNN classifier), which determinates the edges of the network skeleton. We train a CNN on a normalized empirical probability density distribution (NEPDF) for predicting causal interactions and relationships. We have to find the optimal Bayesian network structure for causal inference. Indeed, we are undertaking a search for pair-wise causality, depending on considered causal assumptions. In order to avoid unreasonable causal structure, we consider a blacklist and a whitelist of causality senses. We tested the method on real data to assess the influence of education on the voting intention for the extreme right-wing party. We show that, with this method, we get a safer causal structure of variables (Bayesian Network) and make to identify a variable that satisfies the backdoor criterion.

Keywords: Bayesian network, structure learning, optimal search, convolutional neural network, causal inference

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Authors: J. Fernandez, N. Aguilar, R. Fernandez de Canete, J. C. Ramos-Diaz

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In this paper, a simulation model of the glucose-insulin system for a patient undergoing diabetes Type 1 is developed by using a causal modeling approach under system dynamics. The OpenModelica simulation environment has been employed to build the so called causal model, while the glucose-insulin model parameters were adjusted to fit recorded mean data of a diabetic patient database. Model results under different conditions of a three-meal glucose and exogenous insulin ingestion patterns have been obtained. This simulation model can be useful to evaluate glucose-insulin performance in several circumstances, including insulin infusion algorithms in open-loop and decision support systems in closed-loop.

Keywords: causal modeling, diabetes, glucose-insulin system, diabetes, causal modeling, OpenModelica software

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Authors: Maali Kachouri, Anis Jarboui

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We focus on the causal relationship between governance and information transparency as well as interrelation among the various governance mechanisms. This paper employs a simultaneous equations approach to show this relationship in the Tunisian context. Based on an 8-year dataset, our sample covers 28 listed companies over 2006-2013. Our findings suggest that internal and external governance mechanisms are interdependent. Moreover, in order to analyze the causal effect between information transparency and governance mechanisms, we found evidence that information transparency tends to increase good corporate governance practices.

Keywords: simultaneous equations approach, transparency, causal relationship, corporate governance

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Authors: Li Yin, Xiaoqin Wang

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Background and purpose: Cancer diagnosis is part of a complex stochastic process, in which patients' personal and social characteristics influence the choice of diagnosing methods, diagnosing methods, in turn, influence the initial assessment of cancer stage, the initial assessment, in turn, influences the choice of treating methods, and treating methods in turn influence cancer outcomes such as cancer survival. To evaluate diagnosing methods, one needs to estimate and test the causal effect of a regime of cancer diagnosis and treatments. Recently, Wang and Yin (Annals of statistics, 2020) derived a new general formula, which expresses these causal effects in terms of the point effects of treatments in single-point causal inference. As a result, it is possible to estimate and test these causal effects via point effects. The purpose of the work is to estimate and test causal effects under various regimes of cancer diagnosis and treatments via point effects. Challenges and solutions: The cancer stage has influences from earlier diagnosis as well as on subsequent treatments. As a consequence, it is highly difficult to estimate and test the causal effects via standard parameters, that is, the conditional survival given all stationary covariates, diagnosing methods, cancer stage and prognosis factors, treating methods. Instead of standard parameters, we use the point effects of cancer diagnosis and treatments to estimate and test causal effects under various regimes of cancer diagnosis and treatments. We are able to use familiar methods in the framework of single-point causal inference to accomplish the task. Achievements: we have applied this method to stomach cancer survival from a clinical study in Sweden. We have studied causal effects under various regimes, including the optimal regime of diagnosis and treatments and the effect moderation of the causal effect by age and gender.

Keywords: cancer diagnosis, causal effect, point effect, G-formula, sequential causal effect

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Authors: Hyun-E Yeom

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Patients’ views about the cause of hypertension may influence their present and proactive behaviors to regulate high blood pressure. This study aimed to examine the internal structure underlying the causal beliefs about hypertension and the influence of causal beliefs on self-care intention and medical compliance in Korean patients with hypertension. The causal beliefs of 145 patients (M age = 57.7) were assessed using the Illness Perception Questionnaire-Revised. An exploratory factor analysis was used to identify the factor structure of the causal beliefs, and the factors’ influence on self-care intention and medication compliance was analyzed using multiple and logistic regression analyses. The four-factor structure including psychological, fate-related, risk and habitual factors was identified and the psychological factor was the most representative component of causal beliefs. The risk and fate-related factors were significant factors affecting lower intention to engage in self-care and poor compliance with medication regimens, respectively. The findings support the critical role of causal beliefs about hypertension in driving patients’ current and future self-care behaviors. This study highlights the importance of educational interventions corresponding to patients’ awareness of hypertension for improving their adherence to a healthy lifestyle and medication regimens.

Keywords: hypertension, self-care, beliefs, medication compliance

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Authors: Xiaoqin Wang, Li Yin

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Background and purpose: In many practices, one estimates causal effects arising from a complex stochastic process, where a sequence of treatments are assigned to influence a certain outcome of interest, and there exist time-dependent covariates between treatments. When covariates are plentiful and/or continuous, statistical modeling is needed to reduce the huge dimensionality of the problem and allow for the estimation of causal effects. Recently, Wang and Yin (Annals of statistics, 2020) derived a new general formula, which expresses these causal effects in terms of the point effects of treatments in single-point causal inference. As a result, it is possible to conduct the modeling via point effects. The purpose of the work is to study the modeling of these causal effects via point effects. Challenges and solutions: The time-dependent covariates often have influences from earlier treatments as well as on subsequent treatments. Consequently, the standard parameters – i.e., the mean of the outcome given all treatments and covariates-- are essentially all different (null paradox). Furthermore, the dimension of the parameters is huge (curse of dimensionality). Therefore, it can be difficult to conduct the modeling in terms of standard parameters. Instead of standard parameters, we have use point effects of treatments to develop likelihood-based parametric approach to the modeling of these causal effects and are able to model the causal effects of a sequence of treatments by modeling a small number of point effects of individual treatment Achievements: We are able to conduct the modeling of the causal effects from a sequence of treatments in the familiar framework of single-point causal inference. The simulation shows that our method achieves not only an unbiased estimate for the causal effect but also the nominal level of type I error and a low level of type II error for the hypothesis testing. We have applied this method to a longitudinal study of COVID-19 mortality among Scandinavian countries and found that the Swedish approach performed far worse than the other countries' approach for COVID-19 mortality and the poor performance was largely due to its early measure during the initial period of the pandemic.

Keywords: causal effect, point effect, statistical modelling, sequential causal inference

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Authors: Alasdair Stanton

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What makes for a good explanation? Twenty years after Wendt’s important treatment of constitution and causation, non-causal explanations (sometimes referred to as ‘understanding’, or ‘descriptive inference’) have become, if not mainstream, at least accepted within International Relations. This article proceeds in two parts: firstly, it examines closely Wendt’s constitutional claims, and while it agrees there is a difference between causal and constitutional, rejects the view that constitutional explanations lack temporality. In fact, this author concludes that a constitutional argument is only possible if it relies upon a more foundational, causal argument. Secondly, through theoretical analysis of the constitutional argument, this research seeks to delineate temporal and non-temporal ways of explaining within International Relations. This article concludes that while the constitutional explanation, like other logical arguments, including comparative, and counter-factual, are not truly non-causal explanations, they are not bound as tightly to the ‘real world’ as temporal arguments such as cause-effect, process tracing, or even interpretivist accounts. However, like mathematical models, non-temporal arguments should aim for empirical testability as well as internal consistency. This work aims to give clear theoretical grounding to those authors using non-temporal arguments, but also to encourage them, and their positivist critics, to engage in thoroughgoing empirical tests.

Keywords: causal explanation, constitutional understanding, empirical, temporality

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Authors: Abdullah Alshehab

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Governments worldwide are intensely focused on digitizing public transactions to establish reliable e-government services. The advent of new digital technologies and ongoing advancements in ICT have profoundly transformed business operations. Citizen engagement and participation in e-government services are crucial for the system's success. However, it is essential to measure and enhance citizen satisfaction levels to effectively evaluate and improve these systems. Citizen satisfaction is a key criterion that allows government institutions to assess the quality of their services. There is a strong connection between information quality, service quality, and system quality, all of which directly impact user satisfaction. Additionally, both system quality and information quality have indirect effects on citizen satisfaction. A causal map, which is a network diagram representing causes and effects, can illustrate these relationships. According to the literature, the main factors influencing citizen satisfaction are trust, reliability, citizen support, convenience, and transparency. This paper investigates the causal relationships among these factors and identifies any interrelatedness between them.

Keywords: e-government services, e-satisfaction, citizen satisfaction, causal map.

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Authors: Anna Guitart Atienza, Ana Fernández del Río, Madhav Nekkar, Jelena Ljubicic, África Periáñez, Eura Shin, Lauren Bellhouse

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The use of digital devices in healthcare or mobile health (mHealth) has increased in recent years due to the advances in digital technology, making it possible to nudge healthy behaviors through individual interventions. In addition, mHealth is becoming essential in poor-resource settings due to the widespread use of smartphones in areas where access to professional healthcare is limited. In this work, we evaluate mHealth interventions in low-income countries with a focus on causal inference. Counterfactuals estimation and other causal computations are key to determining intervention success and assisting in empirical decision-making. Our main purpose is to personalize treatment recommendations and triage patients at the individual level in order to maximize the entire intervention's impact on the desired outcome. For this study, collected data includes mHealth individual logs from front-line healthcare workers, electronic health records (EHR), and external variables data such as environmental, demographic, and geolocation information.

Keywords: causal inference, mHealth, intervention, personalization

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Authors: Flora Babongo, Valerie Chavez

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Inferring causality from observational data is one of the fundamental subjects, especially in quantitative finance. So far most of the papers analyze additive noise models with either linearity, nonlinearity or Gaussian noise. We fill in the gap by providing a nonlinear and non-gaussian causal multiplicative noise model that aims to distinguish the cause from the effect using a two steps method based on Bayesian additive models for location, scale and shape (BAMLSS) and on causal additive models (CAM). We have tested our method on simulated and real data and we reached an accuracy of 0.86 on average. As real data, we considered the causality between financial indices such as S&P 500, Nasdaq, CAC 40 and Nikkei, and companies' log-returns. Our results can be useful in inferring causality when the data is heteroskedastic or non-injective.

Keywords: causal inference, DAGs, BAMLSS, financial index

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Authors: Mohamed Raouf Benmakrelouf, Joseph Rynkiewicz

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Targeted maximum likelihood estimation (TMLE) is well-established method for causal effect estimation with desirable statistical properties. TMLE is a doubly robust maximum likelihood based approach that includes a secondary targeting step that optimizes the target statistical parameter. A causal interpretation of the statistical parameter requires assumptions of the Rubin causal framework. The causal effect of binary variable, E, on outcomes, Y, is defined in terms of comparisons between two potential outcomes as E[YE=1 − YE=0]. Our aim in this paper is to present an adaptation of TMLE methodology to estimate the causal effect of a non-binary categorical variable, providing a large application. We propose coding on the initial data in order to operate a binarization of the interest variable. For each category, we get a transformation of the non-binary interest variable into a binary variable, taking value 1 to indicate the presence of category (or group of categories) for an individual, 0 otherwise. Such a dummy variable makes it possible to have a pair of potential outcomes and oppose a category (or a group of categories) to another category (or a group of categories). Let E be a non-binary interest variable. We propose a complete disjunctive coding of our variable E. We transform the initial variable to obtain a set of binary vectors (dummy variables), E = (Ee : e ∈ {1, ..., |E|}), where each vector (variable), Ee, takes the value of 0 when its category is not present, and the value of 1 when its category is present, which allows to compute a pairwise-TMLE comparing difference in the outcome between one category and all remaining categories. In order to illustrate the application of our strategy, first, we present the implementation of TMLE to estimate the causal effect of non-binary variable on outcome using simulated data. Secondly, we apply our TMLE adaptation to survey data from the French Political Barometer (CEVIPOF), to estimate the causal effect of education level (A five-level variable) on a potential vote in favor of the French extreme right candidate Jean-Marie Le Pen. Counterfactual reasoning requires us to consider some causal questions (additional causal assumptions). Leading to different coding of E, as a set of binary vectors, E = (Ee : e ∈ {2, ..., |E|}), where each vector (variable), Ee, takes the value of 0 when the first category (reference category) is present, and the value of 1 when its category is present, which allows to apply a pairwise-TMLE comparing difference in the outcome between the first level (fixed) and each remaining level. We confirmed that the increase in the level of education decreases the voting rate for the extreme right party.

Keywords: statistical inference, causal inference, super learning, targeted maximum likelihood estimation

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Authors: Beatriz Delgado

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Although social anxiety is one of the most prevalent disorders in adolescents and causes considerable difficulties and social distress in those with the disorder, to date very few studies have explored the impact of social anxiety on academic adjustment in student populations. The aim of this study was analyze the effect of social anxiety on school functioning in Secondary Education. Specifically, we examined the relationship between social anxiety and self-concept, academic goals, causal attributions, intellectual aptitudes, and learning strategies, personality traits, and academic performance, with the purpose of creating a causal-explanatory model of academic performance. The sample consisted of 2,022 students in the seven to ten grades of Compulsory Secondary Education in Spain (M = 13.18; SD = 1.35; 51.1% boys). We found that: (a) social anxiety has a direct positive effect on internal attributional style, and a direct negative effect on self-concept. Social anxiety also has an indirect negative effect on internal causal attributions; (b) prior performance (first academic trimester) exerts a direct positive effect on intelligence, achievement goals, academic self-concept, and final academic performance (third academic trimester), and a direct negative effect on internal causal attributions. It also has an indirect positive effect on causal attributions (internal and external), learning goals, achievement goals, and study strategies; (c) intelligence has a direct positive effect on learning goals and academic performance (third academic trimester); (d) academic self-concept has a direct positive effect on internal and external attributional style. Also, has an indirect effect on learning goals, achievement goals, and learning strategies; (e) internal attributional style has a direct positive effect on learning strategies and learning goals. Has a positive but indirect effect on achievement goals and learning strategies; (f) external attributional style has a direct negative effect on learning strategies and learning goals and a direct positive effect on internal causal attributions; (g) learning goals have direct positive effect on learning strategies and achievement goals. The structural equation model fit the data well (CFI = .91; RMSEA = .04), explaining 93.8% of the variance in academic performance. Finally, we emphasize that the new causal-explanatory model proposed in the present study represents a significant contribution in that it includes social anxiety as an explanatory variable of cognitive-motivational constructs.

Keywords: academic performance, adolescence, cognitive-motivational variables, social anxiety

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Authors: James Moir

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The mainstay of Big Data is prediction in that it allows practitioners, researchers, and policy analysts to predict trends based upon the analysis of large and varied sources of data. These can range from changing social and political opinions, patterns in crimes, and consumer behaviour. Big Data has therefore shifted the criterion of success in science from causal explanations to predictive modelling and simulation. The 19th-century science sought to capture phenomena and seek to show the appearance of it through causal mechanisms while 20th-century science attempted to save the appearance and relinquish causal explanations. Now 21st-century science in the form of Big Data is concerned with the prediction of appearances and nothing more. However, this pulls social science back in the direction of a more rule- or law-governed reality model of science and away from a consideration of the internal nature of rules in relation to various practices. In effect Big Data offers us no more than a world of surface appearance and in doing so it makes disappear any context-specific conceptual sensitivity.

Keywords: big data, appearance, disappearance, surface, epistemology

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Authors: Kawsar Ahmed, Hong Wang

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The study's goal was to estimate the causal mediation impact of tobacco tax before and after price hikes among low-income male smokers, with a particular emphasis on the effect estimating pathways framework for continuous and dichotomous variables. From July to December 2021, a cross-sectional investigation of observational data (n=739) was collected from Bangladeshi low-wage smokers. The Quasi-Bayesian technique, binomial probit model, and sensitivity analysis using a simulation of the computational tools R mediation package had been used to estimate the effect. After a price rise for tobacco products, the average number of cigarettes or bidis sticks taken decreased from 6.7 to 4.56. Tobacco product rising prices have a direct effect on low-income people's decisions to quit or lessen their daily smoking habits of Average Causal Mediation Effect (ACME) [effect=2.31, 95 % confidence interval (C.I.) = (4.71-0.00), p<0.01], Average Direct Effect (ADE) [effect=8.6, 95 percent (C.I.) = (6.8-0.11), p<0.001], and overall significant effects (p<0.001). Tobacco smoking choice is described by the mediated proportion of income effect, which is 26.1% less of following price rise. The curve of ACME and ADE is based on observational figures of the coefficients of determination that asses the model of hypothesis as the substantial consequence after price rises in the sensitivity analysis. To reduce smoking product behaviors, price increases through taxation have a positive causal mediation with income that affects the decision to limit tobacco use and promote low-income men's healthcare policy.

Keywords: causal mediation analysis, directed acyclic graphs, tobacco price policy, sensitivity analysis, pathway estimation

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Authors: Peter Adu

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Background: There is a paucity of mental health research in Ghana. Little is known about the beliefs and attitudes regarding specific mental disorders in Ghana. Method: A vignette study was conducted to examine the relationship between causal attributions, help-seeking, and stigma towards depression and schizophrenia using lay Ghanaians (N = 410). This adapted questionnaire presented two unlabelled vignettes about a hypothetical person with the above disorders for participants to provide their impressions. Next, participants answered questions on beliefs and attitudes regarding this person. Results: The results showed that causal beliefs about mental disorders were related to treatment options and stigma: spiritual causal attributions associated positively with spiritual help-seeking and perceived stigma for the mental disorders, whilst biological and psychosocial causal attribution of the mental disorders was positively related with professional help-seeking. Finally, contrary to previous literature, belonging to a particular religious group did not negatively associate with professional help-seeking for mental disorders. Conclusion: In conclusion, results suggest that Ghanaians may benefit from exposure to corrective information about depression and schizophrenia. Our findings have implications for mental health literacy and anti-stigma campaigns in Ghana and other developing countries in the region.

Keywords: stigma, mental health literacy, depression, schizophrenia, spirituality, religion

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Authors: Rachel C. F. Sun

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This study aimed to examine the similarities and differences between teachers’ and students’ causal explanations of classroom misbehavior. In-depth semi-structured interviews were conducted with twelve teachers and eighteen Grade 7-9 students. The qualitative data were analyzed, in which the attributed causes of classroom misbehavior were categorized into student, family, school, and peer factors. Findings showed that both interviewed teachers and students shared similarity in attributing to student factors, such as ‘fun and pleasure seeking’ and ‘attention seeking’ as the leading causes of misbehavior. However, the students accounted to school factors, particularly ‘boring lessons’ as the next attributed causes, while the teachers accounted to family factors, such as ‘lack of parent demandingness’. By delineating the factors at student, family, school, and peer levels, these findings help drawing corresponding implications for preventing and mitigating misbehavior in school.

Keywords: causal explanation, misbehavior, student, teacher

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Authors: Oke Oktavianty, Tadayuki Kyoutani, Shigeyuki Haruyama, Junji Kaneko, Ken Kaminishi

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Modelica has many advantages and it is very useful in modeling and simulation especially for the multi-domain with a complex technical system. However, the big obstacle for a beginner is to understand the basic concept and to build a new system model for a real system. In order to understand how to solve the simple circuit model by hand translation and to get a better understanding of how modelica works, we provide a detailed explanation about solver ordering system in horizontal and vertical sorting and make some proposals for improvement. In this study, some difficulties in using modelica software with the original concept and the comparison with Finite Element Method (FEM) approach is discussed. We also present our textual modeling approach using FEM concept for acausal and causal model construction. Furthermore, simulation results are provided that demonstrate the comparison between using textual modeling with original coding in modelica and FEM concept.

Keywords: FEM, a causal model, modelica, horizontal and vertical sorting

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Authors: Mario Gómez, José Carlos Rodríguez

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The causal relationship between energy consumption and economic growth has been an important issue in the economic literature. This paper studies the causal relationship between electricity consumption and economic growth in Mexico for the period of 1971-2011. In so doing, unit root tests and causality test are applied. The results show that the series are stationary in levels and that there is causality running from economic growth to energy consumption. The energy conservation policies have little or no impact on economic growth in México.

Keywords: causality, economic growth, energy consumption, Mexico

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Authors: László Schmidt

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“Post hoc ergo propter hoc” means after it, therefore because of it. In other words: If event Y followed event X, then event Y must have been caused by event X. The question of causation has long been a central theme in philosophical thought, and many different theories have been put forward. However, causality is an essentially contested concept (ECC), as it has no universally accepted definition and is used differently in everyday, scientific, and legal thinking. In the field of law, the question of causality arises mainly in the context of establishing legal liability: in criminal law and in the rules of civil law on liability for damages arising either from breach of contract or from tort. In the study some philosophical theories of causality will be presented and how these theories correlate with legal causality. It’s quite interesting when philosophical abstractions meet the pragmatic demands of jurisprudence. In Hungarian criminal judicial practice the principle of equivalence of conditions is the generally accepted and applicable standard of causation, where all necessary conditions are considered equivalent and thus a cause. The idea is that without the trigger, the subsequent outcome would not have occurred; all the conditions that led to the subsequent outcome are equivalent. In the case where the trigger that led to the result is accompanied by an additional intervening cause, including an accidental one, independent of the perpetrator, the causal link is not broken, but at most the causal link becomes looser. The importance of the intervening causes in the outcome should be given due weight in the imposition of the sentence. According to court practice if the conduct of the offender sets in motion the causal process which led to the result, it does not exclude his criminal liability and does not interrupt the causal process if other factors, such as the victim's illness, may have contributed to it. The concausa does not break the chain of causation, i.e. the existence of a causal link establish the criminal liability of the offender. Courts also adjudicates that if an act is a cause of the result if the act cannot be omitted without the result being omitted. This essentially assumes a hypothetical elimination procedure, i.e. the act must be omitted in thought and then examined to see whether the result would still occur or whether it would be omitted. On the substantive side, the essential condition for establishing the offence is that the result must be demonstrably connected with the activity committed. The provision on the assessment of the facts beyond reasonable doubt must also apply to the causal link: that is to say, the uncertainty of the causal link between the conduct and the result of the offence precludes the perpetrator from being held liable for the result. Sometimes, however, the courts do not specify in the reasons for their judgments what standard of causation they apply, i.e. on what basis they establish the existence of (legal) causation.

Keywords: causation, Hungarian criminal law, responsibility, philosophy of law

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Authors: Md Yeasin, Ranjit Kumar Paul

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In recent years, recommendation systems have become indispensable tools for agricultural system. The accurate and timely recommendations can significantly impact crop yield and overall productivity. Causal inference modeling aims to establish cause-and-effect relationships by identifying the impact of variables or factors on outcomes, enabling more accurate and reliable recommendations. New advancements in causal inference models have been found in the literature. With the advent of the modern era, deep learning and machine learning models have emerged as efficient tools for modeling. This study proposed an innovative approach to enhance recommendation systems-based machine learning based casual inference model. By considering the causal effect and opportunity cost of covariates, the proposed system can provide more reliable and actionable recommendations for cereal farmers. To validate the effectiveness of the proposed approach, experiments are conducted using cereal cultivation data of eastern India. Comparative evaluations are performed against existing correlation-based recommendation systems, demonstrating the superiority of the advanced causal inference modeling approach in terms of recommendation accuracy and impact on crop yield. Overall, it empowers farmers with personalized recommendations tailored to their specific circumstances, leading to optimized decision-making and increased crop productivity.

Keywords: agriculture, casual inference, machine learning, recommendation system

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Authors: Yemane Hailu Fissuh, Zhongzhan Zhang

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In biomedical researches and randomized clinical trials, the most commonly interested outcomes are time-to-event so-called survival data. The importance of robust models in this context is to compare the effect of randomly controlled experimental groups that have a sense of causality. Causal estimation is the scientific concept of comparing the pragmatic effect of treatments conditional to the given covariates rather than assessing the simple association of response and predictors. Hence, the causal effect based semiparametric transformation model was proposed to estimate the effect of treatment with the presence of possibly time-varying covariates. Due to its high flexibility and robustness, the semiparametric transformation model which shall be applied in this paper has been given much more attention for estimation of a causal effect in modeling left-truncated and right censored survival data. Despite its wide applications and popularity in estimating unknown parameters, the maximum likelihood estimation technique is quite complex and burdensome in estimating unknown parameters and unspecified transformation function in the presence of possibly time-varying covariates. Thus, to ease the complexity we proposed the modified estimating equations. After intuitive estimation procedures, the consistency and asymptotic properties of the estimators were derived and the characteristics of the estimators in the finite sample performance of the proposed model were illustrated via simulation studies and Stanford heart transplant real data example. To sum up the study, the bias of covariates was adjusted via estimating the density function for truncation variable which was also incorporated in the model as a covariate in order to relax the independence assumption of failure time and truncation time. Moreover, the expectation-maximization (EM) algorithm was described for the estimation of iterative unknown parameters and unspecified transformation function. In addition, the causal effect was derived by the ratio of the cumulative hazard function of active and passive experiments after adjusting for bias raised in the model due to the truncation variable.

Keywords: causal estimation, EM algorithm, semiparametric transformation models, time-to-event outcomes, time-varying covariate

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Authors: Lee Yan Nian

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Safety investigation is different from an administrative investigation in that the former is conducted by an independent agency and the purpose of such investigation is to prevent accidents in the future and not to apportion blame or determine liability. Before October 2018, Taiwan railway occurrences were investigated by local supervisory authority. Characteristics of this kind of investigation are that enforcement actions, such as administrative penalty, are usually imposed on those persons or units involved in occurrence. On October 21, 2018, due to a Taiwan Railway accident, which caused 18 fatalities and injured another 267, establishing an agency to independently investigate this catastrophic railway accident was quickly decided. The Taiwan Transportation Safety Board (TTSB) was then established on August 1, 2019 to take charge of investigating major aviation, marine, railway and highway occurrences. The objective of this study is to assess the effectiveness of safety investigations conducted by the TTSB. In this study, the major railway occurrence investigation reports published by the TTSB are used for modeling and analysis. According to the classification of railway occurrences investigated by the TTSB, accident types of Taiwan railway occurrences can be categorized into: derailment, fire, Signal Passed at Danger and others. A Causal Factor Analysis System (CFAS) developed by the TTSB is used to identify the influencing causal factors and their causal relationships in the investigation reports. All terminologies used in the CFAS are equivalent to the Human Factors Analysis and Classification System (HFACS) terminologies, except for “Technical Events” which was added to classify causal factors resulting from mechanical failure. Accordingly, the Bayesian network structure of each occurrence category is established based on the identified causal factors in the CFAS. In the Bayesian networks, the prior probabilities of identified causal factors are obtained from the number of times in the investigation reports. Conditional Probability Table of each parent node is determined from domain experts’ experience and judgement. The resulting networks are quantitatively assessed under different scenarios to evaluate their forward predictions and backward diagnostic capabilities. Finally, the established Bayesian network of derailment is assessed using investigation reports of the same accident which was investigated by the TTSB and the local supervisory authority respectively. Based on the assessment results, findings of the administrative investigation is more closely tied to errors of front line personnel than to organizational related factors. Safety investigation can identify not only unsafe acts of individual but also in-depth causal factors of organizational influences. The results show that the proposed methodology can identify differences between safety investigation and administrative investigation. Therefore, effective intervention strategies in associated areas can be better addressed for safety improvement and future accident prevention through safety investigation.

Keywords: administrative investigation, bayesian network, causal factor analysis system, safety investigation

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Authors: Abdolamir Gatezadeh, Jamal Daghaleh

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The lifestyle of individuals is important and determinant for the status of psychological and social health. Recently, especially in developed countries, the relationship between lifestyle and mental illnesses, including depression, has attracted the attention of many people. In order to test the causal model of depression based on lifestyle with mediation of social health in the study, basic and applied methods were used in terms of objective and descriptive-field as well as the data collection. Methods: This study is a basic research type and is in the framework of correlational plans. In this study, the population includes all adults in Ahwaz city. A randomized, multistage sampling of 384 subjects was selected as the subjects. Accordingly, the data was collected and analyzed using structural equation modeling. Results: In data analysis, path analysis indicated the confirmation of the assumed model fit of research. This means that subscales lifestyle has a direct effect on depression and subscales lifestyle through the mediation of social health which in turn has an indirect effect on depression. Discussion and conclusion: According to the results of the research, the depression can be used to explain the components of the lifestyle and social health.

Keywords: depression, subscales lifestyle, social health, causal model

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Authors: Katharina Buttenberg

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Customer and brand-oriented capabilities have been identified as key influencing capabilities for business performance. Especially in the early years of the firm, it is crucial to develop and consciously manage these capabilities. In this paper, the results of a quantitative analysis, investigating the causal relationship between customer- and brand-oriented (marketing) capabilities and business performance will be presented. The research displays the dependencies between the constructs and will provide practical implications for young firms in the acquisition and management of these capabilities.

Keywords: brand-oriented capabilities, customer-oriented capabilities, entrepreneurship, resource-based theory, young firms

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Authors: Yemane Hailu Fissuh, Zhongzhan Zhang

Abstract:

a systematic observation from a defined time of origin up to certain failure or censor is known as survival data. Survival analysis is a major area of interest in biostatistics and biomedical researches. At the heart of understanding, the most scientific and medical research inquiries lie for a causality analysis. Thus, the main concern of this study is to investigate the causal effect of treatment on survival time conditional to the possibly time-varying covariates. The theory of causality often differs from the simple association between the response variable and predictors. A causal estimation is a scientific concept to compare a pragmatic effect between two or more experimental arms. To evaluate an average treatment effect on survival outcome, the estimating equation was adjusted for time-varying covariates under the semi-parametric transformation models. The proposed model intuitively obtained the consistent estimators for unknown parameters and unspecified monotone transformation functions. In this article, the proposed method estimated an unbiased average causal effect of treatment on survival time of interest. The modified estimating equations of semiparametric transformation models have the advantage to include the time-varying effect in the model. Finally, the finite sample performance characteristics of the estimators proved through the simulation and Stanford heart transplant real data. To this end, the average effect of a treatment on survival time estimated after adjusting for biases raised due to the high correlation of the left-truncation and possibly time-varying covariates. The bias in covariates was restored, by estimating density function for left-truncation. Besides, to relax the independence assumption between failure time and truncation time, the model incorporated the left-truncation variable as a covariate. Moreover, the expectation-maximization (EM) algorithm iteratively obtained unknown parameters and unspecified monotone transformation functions. To summarize idea, the ratio of cumulative hazards functions between the treated and untreated experimental group has a sense of the average causal effect for the entire population.

Keywords: a modified estimation equation, causal effect, semiparametric transformation models, survival analysis, time-varying covariate

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Authors: Misagh Faezipour

Abstract:

System dynamics is a systems engineering approach that can help address the complex challenges in different systems. Little is known about how the brain represents people to predict behavior. This work is based on how the brain simulates different personal behavior and responds to them in the case of young children ages one to five. As we know, children’s minds/brains are just as clean as a crystal, and throughout time, in their surroundings, families, and education center, they grow to develop and have different kinds of behavior towards the world and the society they live in. Hence, this work aims to identify how young children respond to various personality behavior and observes their reactions towards them from a system dynamics perspective. We will be exploring the Big Five personality traits in young children. A causal model is developed in support of the system dynamics approach. These models graphically present the factors and factor relationships that contribute to the big five personality traits and provide a better understanding of the entire behavior model. A simulator will be developed that includes a set of causal model factors and factor relationships. The simulator models the behavior of different factors related to personality traits and their impacts and can help make more informed decisions in a risk-free environment.

Keywords: personality traits, systems engineering, system dynamics, causal model, behavior model

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Authors: Soo Hyung Kim

Abstract:

Two different learning approaches were suggested: focusing on factual knowledge or focusing on the embedded meaning in the statements. Each way of learning has positive effects on different question categories, where factual knowledge helps more with simple fact questions, and searching for meaning in given information helps learn causal relationship and the embedded meaning. To test this belief, two groups of learners (12 male and 39 female adults aged 18-37) watched a ten-minute long Youtube video about various factual events of American history, their meaning, and the causal relations of the events. The fact group was asked to focus on factual knowledge in the video, and the meaning group was asked to focus on the embedded meaning in the video. After watching the video, both groups took multiple-choice questions, which consisted of 10 questions asking the factual knowledge addressed in the video and 10 questions asking embedded meaning in the video, such as the causal relationship between historical events and the significance of the event. From ANCOVA analysis, it was found that the factual knowledge showed higher performance on the factual questions than the meaning group, although there was no group difference on the questions about the meaning between the two groups. The finding suggests that teacher instruction plays an important role in learners constructing a different type of knowledge in online learning.

Keywords: factual knowledge, instruction, meaning-based knowledge, online learning

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Authors: Aliasghar Mehdizadeh Dastjerdi, Francisco Camara Pereira

Abstract:

The increasing complexity and demand of transport services strains transportation systems especially in urban areas with limited possibilities for building new infrastructure. The solution to this challenge requires changes of travel behavior. One of the proposed means to induce such change is multimodal travel apps. This paper describes a study of the intention to use a real-time multi-modal travel app aimed at motivating travel behavior change in the Greater Copenhagen Region (Denmark) toward promoting sustainable transport options. The proposed app is a multi-faceted smartphone app including both travel information and persuasive strategies such as health and environmental feedback, tailoring travel options, self-monitoring, tunneling users toward green behavior, social networking, nudging and gamification elements. The prospective for mobility management travel apps to stimulate sustainable mobility rests not only on the original and proper employment of the behavior change strategies, but also on explicitly anchoring it on established theoretical constructs from behavioral theories. The theoretical foundation is important because it positively and significantly influences the effectiveness of the system. However, there is a gap in current knowledge regarding the study of mobility-management travel app with support in behavioral theories, which should be explored further. This study addresses this gap by a social cognitive theory‐based examination. However, compare to conventional method in technology adoption research, this study adopts a reverse approach in which the associations between theoretical constructs are explored by Max-Min Hill-Climbing (MMHC) algorithm as a hybrid causal discovery method. A technology-use preference survey was designed to collect data. The survey elicited different groups of variables including (1) three groups of user’s motives for using the app including gain motives (e.g., saving travel time and cost), hedonic motives (e.g., enjoyment) and normative motives (e.g., less travel-related CO2 production), (2) technology-related self-concepts (i.e. technophile attitude) and (3) use Intention of the travel app. The questionnaire items led to the formulation of causal relationships discovery to learn the causal structure of the data. Causal relationships discovery from observational data is a critical challenge and it has applications in different research fields. The estimated causal structure shows that the two constructs of gain motives and technophilia have a causal effect on adoption intention. Likewise, there is a causal relationship from technophilia to both gain and hedonic motives. In line with the findings of the prior studies, it highlights the importance of functional value of the travel app as well as technology self-concept as two important variables for adoption intention. Furthermore, the results indicate the effect of technophile attitude on developing gain and hedonic motives. The causal structure shows hierarchical associations between the three groups of user’s motive. They can be explained by “frustration-regression” principle according to Alderfer's ERG (Existence, Relatedness and Growth) theory of needs meaning that a higher level need remains unfulfilled, a person may regress to lower level needs that appear easier to satisfy. To conclude, this study shows the capability of causal discovery methods to learn the causal structure of theoretical model, and accordingly interpret established associations.

Keywords: travel app, behavior change, persuasive technology, travel information, causality

Procedia PDF Downloads 132