• 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.

• 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.

• 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 Korean Institute of Landscape Architecture
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• v.18 no.3 s.39
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• pp.143-154
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• 1990

This study aims at identifying factors and variables which have significant effects on users satisfaction with recreational activities and facilities in Apsan city natural park and therby establishing indicies of planning and / or development of urban forest. To test the causal models of this research, The data were gathered by self-administered questionnaires from 1,147 households in Taegu city which were selected by the multi-stage probabiling sampling method. The analysis of the data primarily consist of two phases : The first analysis dealt with exploratory factor analysis which identified major factors involved in satisfaction with recreational activities and facilities in Apasn city natural park and the second analysis tested the fit of causal models of this research to the data using LISREL methodology. The factor analysis identified that three significant factors are involved in satisfaction with recreational activities and five significant factors are inherent in satisfaction with recreational facilities. The second phase analysis tested the fit of the causal models for satisfaction with recreational activities and facilities to the data and identified statistically significant causal linkage among overall satisfaction with the park, other indogenous factors and exogenous variables. These significant relationships represent important factors and variables that should be considered in planning and/of development of the city natural park. On the basis of there significant causal relationships implications for planning and/or development of the city natural park were suggested.

• Journal of the Korean Institute of Landscape Architecture
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• v.20 no.1
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• pp.29-38
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• 1992

The primary objective of this study is to investigate factors and variables which have significant effects on user satisfaction with recreational facilities in Children's Grand Park in Pusan City, theregby to establish the developmental way of urban park and open space. To test the causal models of this research, the data were gathered by self-administered questionnaires from 1085 households in Pusan City which were selected by the multi-stage probability sampling method. The analysis of the data primarily consists of two phase : The fist analysis dealt exploratory factor analysis which identified major factors involved in satisfaction with recreational facilities in Children's Grand Park and the second analysis tested the fit of the causal models of this research by employing LISREL methodology. The factor analysis identified that five factors are involved in satisfaction with recreational facilities. The five factors of satisfaction with recreational facilities are convenience and maintanance facilities, learnded recreational facilities, spaces for repose and relaxation, spaces for active recreation failities, and facilities for health and physical facilities. 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 Children's Grand Park, other endogenous factors and exogenous variables. Overall fits of both causal models were very good. Among endogenous factors, facilities for repose and relaxation, facilities for convenience and maintenance, learnded recreational facilities were identified as having significant effects on overall satisfaction. Exogenous variables which have significant effects on endogenous variables were also identified. These significant relationships indicate important factors and variables that should be considered in planning and development of urban park and open space. On the basis of these significant causal relationships, way for delovepment of urban park and open space were suggested.

• International Journal of Highway Engineering
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• v.14 no.6
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• pp.121-129
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• 2012

PURPOSES : Traffic accidents at intersections have been increased annually so that it is required to examine the causations to reduce the accidents. However, the current existing accident models were developed mainly with non-linear regression models such as Poisson methods. These non-linear regression methods lack to reveal complicated causations for traffic accidents, though they are right choices to study randomness and non-linearity of accidents. Therefore, to reveal the complicated causations of traffic accidents, this study used structural equation methods(SEM). METHODS : SEM used in this study is a statistical technique for estimating causal relations using a combination of statistical data and qualitative causal assumptions. SEM allow exploratory modeling, meaning they are suited to theory development. The method is tested against the obtained measurement data to determine how well the model fits the data. Among the strengths of SEM is the ability to construct latent variables: variables which are not measured directly, but are estimated in the model from several measured variables. This allows the modeler to explicitly capture the unreliability of measurement in the model, which allows the structural relations between latent variables to be accurately estimated. RESULTS : The study results showed that causal factors could be grouped into 3. Factor 1 includes traffic variables, and Factor 2 contains turning traffic variables. Factor 3 consists of other road element variables such as speed limits or signal cycles. CONCLUSIONS : Non-linear regression models can be used to develop accident predictions models. However, they lack to estimate causal factors, because they select only few significant variables to raise the accuracy of the model performance. Compared to the regressions, SEM has merits to estimate causal factors affecting accidents, because it allows the structural relations between latent variables. Therefore, this study used SEM to estimate causal factors affecting accident at urban signalized intersections.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.5
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• pp.450-457
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• 2004

In this research, the automatic synthesis of knowledge models is proposed. which are the basis of the methods using qualitative models adapted widely in fault diagnosis and hazard evaluation of chemical processes. To provide an easy and fast way to construct accurate causal model of the target process, the Role-Behavior modeling method is developed to represent the knowledge of modularized process units. In this modeling method, Fault-Behavior model and Structure-Role model present the relationship of the internal behaviors and faults in the process units and the relationship between process units respectively. Through the multiple modeling techniques, the knowledge is separated into what is independent of process and dependent on process to provide the extensibility and portability in model building, and possibility in the automatic synthesis. By taking advantage of the Role-Behavior Model, an algorithm is proposed to synthesize the plant-wide causal model, Fault-Causality Graph (FCG) from specific Fault-Behavior models of the each unit process, which are derived from generic Fault-Behavior models and Structure-Role model. To validate the proposed modeling method and algorithm, a system for building FCG model is developed on G2, an expert system development tool. Case study such as CSTR with recycle using the developed system showed that the proposed method and algorithm were remarkably effective in synthesizing the causal knowledge models for diagnosis of chemical processes.

• Journal of the Korean Institute of Landscape Architecture
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• v.19 no.3
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• pp.87-97
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• 1991

The primary purpose of this study is to investigate factors and variables which have significant effects on user's satisfaction with recreational activities in Daeshin Natural Park, thereby establishing directions of development of urban parks. To test the causal models of this research, the data were gathered by self-administered questionnaires form 627 households in Pusan City which were selected by the multi-stage probability sampling method. The analysis of the data primarily consists of two-phase: The first analysis dealt exploratory factor analysis which identified major factors involved in satisfaction with recreational activities in Daeshin Natural Park and the second analysis tested the fit of the causal models of this research by employing LISREL methodology. The factor analysis identified that three factors are involved in satisfaction with recreational actitives. The three factors of satisfaction with recreational activities are facilities for health and phisical exercise, group recreational activity, maintenance activity. The second phase analysis tested the fit of the causal models for satisfaction with recreational activity to the data and identified statistically significant causal linkage among overall satisfaction with Daeshin Natural Park, other endogenous factors and exogenous variables.

• Journal of the Korean Institute of Landscape Architecture
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• v.23 no.1
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• pp.39-50
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• 1995

This Study aims at identifying factor and variables which have significant effects on user satisfaction with recreational resources and facilities in Juwang National Park and there-by establishes indicies of planning and / or management of the park. To test the causal models of this research, the data was gathered by self-administered questionnaires from users in Juwang National Park. The analysis of the data was conducted in two phases. The first analysis dealt with exploratory gactor analysis which identified major factors involved in satisfaction with recreational facilities in Juwang National Park. The second analysis tested the fit of causal models of this research to the data using LISREL methodology. The factor analysis identified six significant factors in satisfaction with recreational resources, The six factors are convenient park facilities, natural landacape resources, linear park facilities, mineral springs and Buddhist temples, commercial and sleeping accommodations, rest areas and open space for passive recreational activity. The second phase analysis tested the fit of the causal model for satisfaction with recreation facilities to the data. The phase identified statistically significant causal links among overall satisfaction with the park, and other indigenous factors and exogenous variables. From these causal relationships, implications for future management of Juwang National Park were suggested.

• Communications for Statistical Applications and Methods
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• v.29 no.2
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• pp.177-191
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• 2022

This paper addresses the use of machine learning methods for causal estimation of treatment effects from observational data. Even though conducting randomized experimental trials is a gold standard to reveal potential causal relationships, observational study is another rich source for investigation of exposure effects, for example, in the research of comparative effectiveness and safety of treatments, where the causal effect can be identified if covariates contain all confounding variables. In this context, statistical regression models for the expected outcome and the probability of treatment are often imposed, which can be combined in a clever way to yield more efficient and robust causal estimators. Recently, targeted maximum likelihood estimation and causal random forest is proposed and extensively studied for the use of data-adaptive regression in estimation of causal inference parameters. Machine learning methods are a natural choice in these settings to improve the quality of the final estimate of the treatment effect. We explore how we can adapt the design and training of several machine learning algorithms for causal inference and study their finite-sample performance through simulation experiments under various scenarios. Application to the percutaneous coronary intervention (PCI) data shows that these adaptations can improve simple linear regression-based methods.