• Computers and Concrete
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• v.31 no.4
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• pp.277-292
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• 2023

Much of our experiments are designed to uncover the cause(s) and effect(s) behind a phenomenon (i.e., data generating mechanism) we happen to be interested in. Uncovering such relationships allows us to identify the true workings of a phenomenon and, most importantly, to realize and articulate a model to explore the phenomenon on hand and/or allow us to predict it accurately. Fundamentally, such models are likely to be derived via a causal approach (as opposed to an observational or empirical mean). In this approach, causal discovery is required to create a causal model, which can then be applied to infer the influence of interventions, and answer any hypothetical questions (i.e., in the form of What ifs? Etc.) that commonly used prediction- and statistical-based models may not be able to address. From this lens, this paper builds a case for causal discovery and causal inference and contrasts that against common machine learning approaches - all from a civil and structural engineering perspective. More specifically, this paper outlines the key principles of causality and the most commonly used algorithms and packages for causal discovery and causal inference. Finally, this paper also presents a series of examples and case studies of how causal concepts can be adopted for our domain.

• Smart Media Journal
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• v.12 no.10
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• pp.29-37
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• 2023

Human lifestyle is affected by the agricultural development in the last 12,000 years ago. The development of agriculture is one of the reasons that global population surged. To ensure sufficient food production for supporting human life, pesticides as a more effective and economical tools, are extensively used to enhance the yield quality and boost crop production. This study investigated the factors that affect crop production and whether the factors of pesticide usage are the most important factors in crop production using the dataset from Kaggle that provides information based on crops harvested by various farmers. Logistic regression is used to investigate the relationship between various factors and crop production. However, the logistic regression is unable to deal with predictors that are related to each other and identifying the greatest impact factor. Therefore, causal discovery is applied to address the above limitations. The result of causal discovery showed that crop condition is greatly impacted by the estimated insects count, where estimated insects count is affected by the factors of pesticide usage. This study enhances our understanding of the influence of pesticide usage on crop production and contributes to the progress of agricultural practices.

• Journal of the Korean Society of Safety
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• v.35 no.2
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• pp.34-46
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• 2020

Unlike research focused on existing technologies and individual errors to analyze the causes of incidents, this study approached them from an organization and culture. And this study is not a one way study but cyclical study what can track cause down using causal loop diagram methodology. Four diagnostic criteria for the negative state of the safety culture : secretive, blame, failure to learning, and incremental learning, combine literature study and expert opinion to derive 41 variables. Connecting these variable make 4 causal loop diagrams and total causal loop diagram. Case accumulation in secretive, accident report in blame, knowledge accumulation in failure to learning, near miss discovery in incremental learning are the main variables. Safety incident is the objective variable by classifying them into 4 stages in total loop, leading track as the most affect is case accumulation, and Step 4 as you can see accident report and near miss discovery are the result of tracking down the cause. This study can be used as a basis for improving the management priority and the system in incident prevention.

• Journal of Genetic Medicine
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• v.10 no.1
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• pp.13-19
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• 2013

Retinitis pigmentosa (RP) is the most common hereditary retinal disorder and is characterized by progressive retinal degeneration and decline in vision. RP comprises a heterogeneous group of disorders caused by various genetic variants. Since the first discovery of the causal mutation in the RHO gene using positional cloning, numerous mutations have been detected in more than 60 loci and 50 genes. However, causal genes have not been discovered in about 50% of cases. We attempt here to review the strategies to identify causal alleles of retinitis pigmentosa. These include conventional methods as well as state-of-the-art technologies based on next-generation sequencing.

• Journal of Korea Society of Industrial Information Systems
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• v.22 no.6
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• pp.71-84
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• 2017

Over the past decade, we have witnessed Serious Debates in MISQ and Other Journals Between Two Camps that have Differing Views on the use of Causal Indicators to Measure Constructs. There is the Camp that advocates Causal Indicators (ADVOCATE) and the Camp that opposes Their Usage (OPPONENT). The Debates have been primarily centered on the OPPONENT's Argument that the Meaning of a Latent Variable is determined by its Outcome Variables. However, Little Effort has been made to Validate the ADVOCATE's Dispute (Against the OPPONENT's Arguments) that the Meaning of a Latent Variable is decided by its Causal Indicators if there is no Misspecification. Our Study precisely examines the Integrity of the Argument. For this, we empirically examine how the two Primary Psychometric Properties-Comprehensiveness and Interrelationship-of Causal Indicators Influence Theory Testing between Latent Variables through Three Different Tests (i.e., Comprehensive Test, Interrelationship Test, and Mixed Test). Conducted on Two Different Datasets, Our Analysis Consistently Reveals that Structural Path Coefficients are Hardly Sensitive to the Changes (i.e., Misspecification) in the Properties of Causal Indicators. The Discovery offers Important Evidence that the Sound Theoretical Logic of a Causal Model is not in Sync with the Empirical Mechanism of Parameter Estimation. This Underscores that a Latent Variable Formed by Causal Indicators is empirically an elusive notion that is Difficult to Operationalize. As Our Results have Significant Implications on the Integrity of Numerous IS studies which have conducted Theory or Hypothesis Testing Using Causal Indicators, we strongly advocate Open Discussions among Methodologists regarding Our Findings and Their Implications for Both Published IS Research and Future Practices.

• Environmental and Resource Economics Review
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• v.15 no.5
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• pp.799-826
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• 2006

Combining recent advances in causal flows with time series analysis, relationships among eight North American natural gas spot market prices are examined. Results indicate that price discovery tends to occur in excess demand regions and move to excess supply regions. Across North America, the U.S. Midwest region represented by Chicago spot market is the most important market for price discovery. The Ellisburg-Leidy Hub in Pennsylvania is important in price discovery, especially for markets in the eastern two-thirds of the U.S. Malin Hub in Oregon is important for the western markets including the AECO Hub in Alberta, Canada.

• Knowledge Management Research
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• v.10 no.2
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• pp.49-63
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• 2009

In this study, sub domain of flow was investigated on literature survey, and suggested of the measuring method of web-site flow and its simulation analysis. Constructing of measuring method of flow, and using this method what-if analysis was simulated when several condition changed. Using causal map approach to extract knowledge from web-site domain experts and to derives a causal relationship of knowledge. Specially, in our study, describes method of developing and building causal map, and suggests guide line of this method on practical application. This research results show that web-site flow starts "direct searching" or "interesting of special issue(domain)", and when challenges of web-site were accorded with user's skills web-site flow grows. Further, in the web-site, information searching intention results in increase of user's duration time and experience flow to discovery new interesting issues in this process. If user's web-site of interaction is increased, awareness of environment conditions decreased, finally, user's telepresense results in increased web-site flow. This paper contained thai this method make used of measuring flow in the web-site and developing of practical strategy.

• Asian-Australasian Journal of Animal Sciences
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• v.21 no.5
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• pp.617-623
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• 2008

A fine-mapping method exploiting linkage disequilibrium was used to detect quantitative trait loci (QTL) on the X chromosome affecting milk production, body conformation and productivity traits. The pedigree comprised 22 paternal half-sib families of Black-and-White Holstein bulls in the Netherlands in a grand-daughter design for a total of 955 sons. Twenty-five microsatellite markers were genotyped to construct a linkage map on the chromosome X spanning 170 Haldane cM with an average inter-marker distance of 7.1 cM. A covariance matrix including elements about identical-by-descent probabilities between haplotypes regarding QTL allele effects was incorporated into the animal model, and a restricted maximum-likelihood method was applied for the presence of QTL using the LDVCM program. Significance thresholds were obtained by permuting haplotypes to phenotypes and by using a false discovery rate procedure. Seven QTL responsible for conformation types (teat length, rump width, rear leg set, angularity and fore udder attachment), behavior (temperament) and a mixture of production and health (durable prestation) were detected at the suggestive level. Some QTL affecting teat length, rump width, durable prestation and rear leg set had small numbers of haplotype clusters, which may indicate good classification of alleles for causal genes or markers that are tightly associated with the causal mutation. However, higher maker density is required to better refine the QTL position and to better characterize functionally distinct haplotypes which will provide information to find causal genes for the traits.

• Korean System Dynamics Review
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• v.15 no.4
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• pp.129-159
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• 2014

This Study discuss exploring normative-prescriptive factors after the themes on Organizational learning categorize two descriptive/explanatory-perspectives, prescriptive/normative dimension. The former would contain information processing model, theory of action, organizing in organization, while Senge's suggestion on Learning Organization may compose the latter. Each perspective is reconstructed and reinterpreted into the causal mapping relationship founded on system thinking and SD. Underlying on the former try to discovery validities of the latter. But this study only put forward the integral-dynamic model of organizational learning without empirical simulation.

• Journal of The Korean Association For Science Education
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• v.22 no.2
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• pp.314-335
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• 2002

The purpose of this study was to investigate the effects of students' prior knowledge on scientific reasoning process performing a task of controlling variables with computer simulation and to identify a number of problems that students encounter in scientific discovery. Subjects for this study included 60 Korean students: 27 fifth-grade students from an elementary school; 33 seventh-grade students from a middle school. The sinking objects task involving multivariable causal inference was used. The task was presented as computer simulation. The fifth and seventh-grade students participated individually. A subject was interviewed individually while the investigating a scientific reasoning task. Interviews were videotaped for subsequent analysis. The results of this study indicated that students' prior knowledge had a strong effect on students' experimental intent; the majority of participants focused largely on demonstrating their prior knowledge or their current hypothesis. In addition, studnets' theories that were part of one's prior knowledge had significant impact on formulating hypotheses, testing hypothesis, evaluating evidence, and revising hypothesis. This study suggested that students' performance was characterized by tendencies to generate uninformative experiments, to make conclusion based on inconclusive or insufficient evidence, to ignore, reject, or reinterpret data inconsistent with their prior knowledge, to focus on causal factors and ignore noncausal factors, to have difficulty disconfirming prior knowledge, to have confirmation bias and inference bias (anchoring bias).