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

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

• Korea Journal of Hospital Management
• /
• v.21 no.4
• /
• pp.1-12
• /
• 2016

The purpose of this study are suggest to performance measurement model of Electronic Medical Record(EMR) and Key Performance Index(KPI). For data collection, 665 questionnaires were distributed to medical record administrators and insurance reviewers at 31 hospitals, and 580 questionnaires were collected(collection rate: 87.2%). Regarding methodology, Critical Success Factor(CSF) and index of the information system were derived based on previous studies, and these were set as performance measurement factors of EMR system. The performance measurement factors were constructed by perspective using BSC, and analysis on causal relationship between factors was conducted. A model of causal relationship was established, and performance measurement model of EMR system was proposed through model validation. Analysis on causal relationship between performance management factors revealed that utility cognition of the learning & growth perspective factor had causal relationship with job efficiency(${\beta}=0.20$) and decision support(${\beta}=0.66$) of the internal process perspective factors, and security had causal relationship with system satisfaction(${\beta}=0.31$) of the customer perspective factor. System quality had causal relationship with job efficiency(${\beta}=0.66$) and decision support(${\beta}=0.76$) of the internal process perspective factors, all of which were statistically significant(P<0.01). Job efficiency of the internal process perspective had causal relationship with system satisfaction(${\beta}=0.43$), and decision support had causal relationship with decision support satisfaction(${\beta}=0.91$) and job satisfaction (${\beta}=0.74$), all of which were statistically significant(P<0.01). System satisfaction of the customer perspective had causal relationship with job satisfaction(${\beta}=0.12$), job satisfaction had causal relationship with cost reduction(${\beta}=0.53$) of the financial perspective, and decision support satisfaction had causal relationship with productivity improvement(${\beta}=0.40$)of the financial perspective(P<0.01). Also, cost reduction of the financial perspective had causal relationship with productivity improvement(${\beta}=0.37$), all which were statistically significant(P<0.05). Suitability index verification of the performance measurement model whose causal relationship was found to be statistically significant revealed that $X^2/df=2.875$, RMR=0.036, GFI=0.831, AGFI=0.810, CFI=0.887, NFI=0.838, IFI=0.888, RMSEA=0.057, PNFI=0.781, and PCFI=0.827, all of which were in suitable levels. In conclusion, the performance measurement indices of EMR system include utility cognition, security, and system quality of the learning & growth perspective, decision support and job efficiency of the internal process perspective, system satisfaction, decision support satisfaction, and job satisfaction of the customer perspective, and productivity improvement and cost reduction of the financial perspective. In this study, it is expected that the performance measurement indices and model of EMR system which are suggested by the author, will be a measurement tool available for system performance measurement of EMR system in medical institutions.

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

• Korean Journal of Cognitive Science
• /
• v.21 no.1
• /
• pp.47-75
• /
• 2010

Two experiments were conducted to explore whether the Or structure works as a default causal model in inferring the causal structure from the contingency data. The contingencies of three unfamiliar variables were used in Experiment 1. Participants inferred the Or structure quite well from the OR data, but incorrectly inferred the Or structure from the And data for about a little less than half of the time, and almost always inferred the Or structure from the chain data. The results suggested that the Or interpretation can be the default causal model. The prevalence of the Or interpretation from the contingency data was reported even when the three variables were familiar ones in Experiment 2. Multinomial modeling performed on the results of the two experiments strongly suggested that the Or interpretation work as a default causal model.

• Journal of the Ergonomics Society of Korea
• /
• v.22 no.2
• /
• pp.1-13
• /
• 2003

In general, causal relationship for theoretical concepts is hypothesized based on precedent studies and tested by a structural equation model. However, when theoretical backgrounds are scarce or absent, the causal relationship is hypothesized operatively by the purpose and scope of research and tested by overall goodness-of-fit indices such as GFI and RMR. Such a causal relationship can't be most appropriate statistically because it is selected as specific relationship from researcher's view among possible causal relationships. Therefore, this study is to propose a procedure for identifying the causal relationship that produces the best GFI among possible causal relationships for theoretical concepts.

• Korean Journal of Logic
• /
• v.8 no.1
• /
• pp.47-67
• /
• 2005

Bayesian networks have been used in studying and simulating causal inferences by using the probability function distributed over the variables consisting of inquiry space. The focus of the debates concerning Bayesian networks is the causal Markov condition that constrains the probabilistic independence between all the variables which are not in the causal relations. Cartwright, a strong critic about the Bayesian network theory, argues that the causal Markov condition cannot hold in indeterministic systems, so it cannot be a valid principle for causal inferences. The purpose of the paper is to explore whether her argument on the causal Markov condition is valid. Mainly, I shall argue that it is possible for upholders of the causal Markov condition to respond properly the criticism of Cartwright through the continuous causal model that permits the infinite sequence of causal events.

• Korean System Dynamics Review
• /
• v.14 no.3
• /
• pp.75-103
• /
• 2013

The purpose of this study is to use system dynamics to establish the relation among each variable through the construction process of Community Planning Model, and examine what changes policy scenarios per alternative cause in Community Planning through policy simulation of the constructed model. Therefore, this study extracted chief variables of Community Planning Projects through precedent researches related to Community Planning, and extracted variables were prepared as causal map to examine in what causal cycle feedback structure within Community Planning they can be explained. Next, Community Planning Model was constructed based on the prepared causal map. The model was verified by specialists' interviews and simulation of example areas. This study, which aimed to construct Community Planning Model using system dynamics, has a significance in that it prepared the foundation to provide useful methodology in monitoring the progress of project or establishing the plan of future Community Planning Projects.

• The Korean Journal of Applied Statistics
• /
• v.32 no.2
• /
• pp.173-185
• /
• 2019

Causal questions are prevalent in scientific research, for example, how effective a treatment was for preventing an infectious disease, how much a policy increased utility, or which advertisement would give the highest click rate for a given customer. Causal inference theory in statistics interprets those questions as inferring the effect of a given intervention (treatment or policy) in the data generating process. Causal inference has been used in medicine, public health, and economics; in addition, it has received recent attention as a tool for data-driven decision making processes. Many recent datasets are observational, rather than experimental, which makes the causal inference theory more complex. This review introduces key concepts and recent trends of statistical causal inference in observational studies. We first introduce the Neyman-Rubin's potential outcome framework to formularize from causal questions to average treatment effects as well as discuss popular methods to estimate treatment effects such as propensity score approaches and regression approaches. For recent trends, we briefly discuss (1) conditional (heterogeneous) treatment effects and machine learning-based approaches, (2) curse of dimensionality on the estimation of treatment effect and its remedies, and (3) Pearl's structural causal model to deal with more complex causal relationships and its connection to the Neyman-Rubin's potential outcome model.

• Health Policy and Management
• /
• v.8 no.1
• /
• pp.52-96
• /
• 1998

To find causal factors and improvement plans of organizational commitment of public servants in urban health centers, a hypothetical causal model, which included 2 endogenous variables(organizational commitment & organizational satisfaction) and 15 exogenous variables, was constructed. Exogenous variables consisted of individual factors (sex, age, education, job-grade, and annual salary), psychological variables(pride for organization, extrinsic motivation, intrinsic motivation and support of supervisor) ad structural variables(formalization, centralization, communication, job-conflict, job-decision, and workload). In the hypothetical causal model, organizational commitment was supposed to be effect variable, and organizational satisfaction was presumed to be intervening variable to mediate between organizational commitment and exogenous variables. For data collection, cross-sectional self-administered questionnaire survey was conducted to 1,295 public servants from 32 urban health centers nationwide. The survey responses were from 934, 72.1% of subjects. But 756 responses(58.4%) were analyzed because of excluding ones with missing values. The hypothetical causal model was fitted by covariance structural analysis with maximum likelihood method. Main results were as follows: (1) The fitted causal model accounted for 33 and 55 percent of total variance of organizational commitment and organizational satisfaction of public servants, respectively. (2) In order of effect size, pride for organization, supervisor support, communication, extrinsic motivation and centralization had an indirect effect effect on organizational commitment through organizational satisfaction. However, the effect of centralization was negative. (3) Pride for organiztion, intrinsic motivation, organizational satisfaction, job-conflict, supervisor support, communication, age, centralization, annual salar and extrinsic motivation had indirect or direct effects on organizational commitment in order of effect size. Among them, effects of job-conflict and centraldization were negative. In conclusion, these results suggested that organizational commitment of public servants in urban health centers could be enhanced by pride for organization, intrinsic and extrinsic motivations, prevention of job-conflict and excess centralization, supervisor support and active communication. Especially, pride for organization and intrinsic motivation were expected to play the most important role.