• Knowledge Management Research
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• v.10 no.2
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• pp.35-48
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• 2009

The paper proposes a quantitative causal ordering map (QCOM) to combine qualitative and quantitative methods in a framework. The procedures for developing QCOM consist of three phases. The first phase is to collect partially known causal dependencies from experts and to convert them into relations and causal nodes of a model graph. The second phase is to find the global causal structure by tracing causality among relation and causal nodes and to represent it in causal ordering graph with signed coefficient. Causal ordering graph is converted into QCOM by assigning regression coefficient estimated from path analysis in the third phase. Experiments with the prediction model of Korea stock price show results as following; First, the QCOM can support the design of qualitative and quantitative model by finding the global causal structure from partially known causal dependencies. Second, the QCOM can be used as an integration tool of qualitative and quantitative model to offerhigher explanatory capability and quantitative measurability. The QCOM with static and dynamic analysis is applied to investigate the changes in factors involved in the model at present as well discrete times in the future.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.4
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• pp.45-52
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• 2023

With the advent of deep learning, Artificial Intelligence (AI) technology has experienced rapid advancements, extending its application across various industrial sectors. However, the focus has shifted from the independent use of AI technology to its dispersion and proliferation through the open AI ecosystem. This shift signifies the transition from a phase of research and development to an era where AI technology is becoming widely accessible to the general public. However, as this dispersion continues, there is an increasing demand for the verification of outcomes derived from AI technologies. Causal AI applies the traditional concept of causal inference to AI, allowing not only the analysis of data correlations but also the derivation of the causes of the results, thereby obtaining the optimal output values. Causal AI technology addresses these limitations by applying the theory of causal inference to machine learning and deep learning to derive the basis of the analysis results. This paper analyzes recent cases of causal AI technology and presents the major tasks and directions of causal AI, extracting patterns between data using the correlation between them and presenting the results of the analysis.

• Korean System Dynamics Review
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• v.10 no.1
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• pp.33-60
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• 2009

This study explores the limitation in making a causal model through an existing case and proposes an alternative plan to improve a theoretical system of causation modeling. To make a dynamic and actual model, several principles are needed such as reality based analysis of system structures and dynamics, consistent expression of causations, conversion of numerical formulas to causal relations, classification and arrangement of variables by size of concept, etc. However, it is hard to find cases to apply these considerations from existing models in System Dynamics. Therefore, this study verifies errors of derived models from literatures and proposes principles and guides that should be considered to make a sound dynamic model on a causal map. It contributes to making an opportunity for exciting public opinion to improve theory about causal maps, yet it has limitation that the study does not advance forward to the experimental step. For future study, it plans to make up by classifying and leveling causal variables, developing a dynamic BSC model.

• Proceedings of the Korean System Dynamics Society
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• 2004.08a
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• pp.109-128
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• 2004

The complexity of information and communication policy has been increasing due to its rapid changes and its expansions toward various fields. I used the2001, 2002 and 2003 White Papers on MIC(Ministry of Information and Communication Republic of Korea) as a reference and the Vensim PLE program to create a causal map. According to my analysis, no major feedback loop was found among the information and communication policies. Thus, it was impossible to conduct a causal map analysis on these policies. The causal map analysis is usually employed to understand a complex mechanism of entire policies by finding feedback loops among them. A lack of feedback loops makes it impossible to conduct the causal map analysis and means that the mechanism of such policies is even more complex to understand. The most important conclusion is that to consider feedback thought among the policies based on the systems thinking before making the policies.

• Language and Information
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• v.8 no.2
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• pp.131-147
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• 2004

the two Korean causal conjunctive suffixes, -nula(ko) and -nun palamey, based on corpus linguistic analysis. Many of the linguistic accounts available, both in pedagogical reference and in the literature on linguistics, provide incomplete analyses of these suffixes, based on fabricated linguistic data. Using naturally occurring, real linguistic data, this paper examines the syntactic and semantic structures of the two causal suffixes through a consideration of three areas of corpus linguistic analysis: token frequencies, collocations, and semantic prosody. An analysis based on concordance data reveals that the two causal connectives, -nula(ko) and -nun palamey, have more differences than similarities in terms of syntactic and semantic constraints. The idiosyncratic structures of the two suffixes are discussed in terms of same subject condition, verb selection, same agent condition, synchronicity condition, and negative semantic prosody.

• Journal of Preventive Medicine and Public Health
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• v.56 no.4
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• pp.303-311
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• 2023

Traditional mediation analysis, which relies on linear regression models, has faced criticism due to its limited suitability for cases involving different types of variables and complex covariates, such as interactions. This can result in unclear definitions of direct and indirect effects. As an alternative, causal mediation analysis using the counterfactual framework has been introduced to provide clearer definitions of direct and indirect effects while allowing for more flexible modeling methods. However, the conceptual understanding of this approach based on the counterfactual framework remains challenging for applied researchers. To address this issue, the present article was written to highlight and illustrate the definitions of causal estimands, including controlled direct effect, natural direct effect, and natural indirect effect, based on the key concept of nested counterfactuals. Furthermore, we recommend using 2 R packages, 'medflex' and 'mediation', to perform causal mediation analysis and provide public health examples. The article also offers caveats and guidelines for accurate interpretation of the results.

• Journal of Preventive Medicine and Public Health
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• v.55 no.2
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• pp.116-124
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• 2022

Epidemiological studies typically examine the causal effect of exposure on a health outcome. Standardization is one of the most straightforward methods for estimating causal estimands. However, compared to inverse probability weighting, there is a lack of user-centric explanations for implementing standardization to estimate causal estimands. This paper explains the standardization method using basic R functions only and how it is linked to the R package stdReg, which can be used to implement the same procedure. We provide a step-by-step tutorial for estimating causal risk differences, causal risk ratios, and causal odds ratios based on standardization. We also discuss how to carry out subgroup analysis in detail.

• The Korean Journal of Applied Statistics
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• v.27 no.7
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• pp.1077-1095
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• 2014

Unlike randomized trial, statistical strategies for inferring the unbiased causal relationship are required in the observational studies. Recently, new methods for the causal inference in the observational studies have been proposed such as the matching with the propensity score or the inverse probability treatment weighting. They have focused on how to control the confounders and how to evaluate the effect of the treatment on the result variable. However, these conventional methods are valid only when the treatment variable is categorical and both of the treatment and the result variables are directly observable. Research on the causal inference can be challenging in part because it may not be possible to directly observe the treatment and/or the result variable. To address this difficulty, we propose a method for estimating the average causal effect when both of the treatment and the result variables are latent. The latent class analysis has been applied to calculate the propensity score for the latent treatment variable in order to estimate the causal effect on the latent result variable. In this work, we investigate the causal effect of adolescents delinquency on their substance use using data from the 'National Longitudinal Study of Adolescent Health'.

• Journal of the Korean Institute of Landscape Architecture
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• v.18 no.4
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• pp.127-140
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• 1991

The Primary purpose of this study is to investigate factors and variables which have significant effects on user satisfaction with recreational facilities in Taejong-Dae recreational complex, thereby establishing indices of planning and development of urban parks and open space. To test the causal models of this research, the date were gathered by self-administered questionnaires from 967 households in Pusan City which were selected by the multi-stage probability sampling methood. The analysis of the multi-stage primarily consists of two phase : The first analysis dealt exploratory factor analysis which identified major factors involved in satisfaction with recreational activities and facilities in Taejong-Dae recreational complex and the second analysis tested the fit of the causal models of this research by employing LISREL methodology. There are three advantages of using LISREL over other multivariate analysis methods : First, measurement error is allowed and calculated in LISREL, otherwise there is a risk of seriously misleading estimates of coefficients ; Second, LISREL deals with latent variables or unmeasured variables ; Third, it enables to test causal relations among variables. The factors analysis identified that five factors are involved in satisfaction with recreational facilities. The five factors of satisfaction with recreational facilities are space for repose and relaxation, active recreation facilities such as pool and zoo, physical exercise facility, convenience and maintenance facility, and linear facility, and linear facility for walking. The second phase analysis tested the fit of the causal models for satisfaction with recreational facilities to the data and identified statistically significant causal linkage among overall satisfaction with Taejong-Dae recreational complex, other endogenous factors and exogenous variables. Overall fits of both causal models were very good. Among endogenous factors, facility for repose and relaxation. linear facility for walking, active recreation facility, facility for convenience and maintenance were identified as having significant effects on overall satisfaction. Exogenous variables which have significant effects on endogenous variables wer also identified. These significant relationships indicate important factors and variables that should be considered in planning and development of the recreational complex. On the basis of these significant causal relationships, implications for planning and the delovepment of Taejong-Dae recreational complex were suggested.

• Journal of the Ergonomics Society of Korea
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• v.32 no.6
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• pp.535-540
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• 2013

Objective: The aim of this study is to evaluate the effectiveness and efficiency of causal links between various error causes in human error analysis. Background: As finding root causes of human error in safety-critical systems is often a cognitively demanding and time-consuming task, it is particularly necessary to develop a method for improving both the quality and efficiency of the task. Although a few methods such as CREAM have suggested causal linking between error causes as a means to enhance the quality and efficiency of human error analysis, no published research to date has evaluated the performance of the causal links. Method: The performance of the CREAM links between error causes were evaluated with 80 railway accident investigation reports from the UK. From each report, errorneous actions of operators were derived, and for each error, candidate causes were found by following the predefined links. Two measures, coverage and selectivity, were used to evaluate the effectiveness and efficiency of the links, respectively. Results: On average, 96% of error causes actually included in the accident reports were found by following the causal links, and among the total of 121 possible error causes, the number of error causes to be examined further was reduced to one-tenth on average. As an additional result of this work, frequent error causes and frequently used links are provided. Conclusion: This result implies that the predefined causal links between error causes can significantly reduce the time and effort required to find the multiple levels of error causes and their causal relations without losing the quality of the results. Application: The CREAM links can be applied to human error analysis in any industry with minor modifications.