• Korean Journal of Logic
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• v.18 no.1
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• pp.83-133
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• 2015

In this paper, I pose a problem for Hitchcock's arguments for two concepts of effects that are intended to explicate double causal effects, and put forth a theory that is intended not just to meet the problem but also to accommodate Hitchcock's theory and Eells' theory both. First, I introduce an example of dual causal effects, and examine the accounts of Otte(1985) and Eells(1987) on how to explicate the dual effects. I show that their accounts of the dual effects help us understand the problem of dual effects and see how different it is for Cartwright(1979, 1989, 1995), Eells(1991, 1995), and Hitchcock(2001a) to meet the problem. Second, I introduce two concepts of effects on Hitchcock(2001a), that is, net effect and component effect that are allegedly analogous to two effects of structural equation model. Third, I reveal the significance of homogeneous subpopulation and causal interaction regarding the problem of dual effects while examining Cartwright's theory and Elles' theory. Fourth, I critically examine the two concepts of effects on Hitchcock and argue against Hitchcock's criticism of Eells' theory. Fifth, I take a moderator variable of structural equation model and a moderator effect into the probabilistic theory of causality, and formally generalize causal interaction due to the dual effects in terms of disjunctive relation and counterfactual conditionals. I expect my account of disjunctive relation and counterfactual conditionals to contribute not just to several problems the received theories of causal modelling confront but also to the structural equation models many people exploit as a promising statistical methodology.

• Environmental and Resource Economics Review
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• v.15 no.3
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• pp.479-504
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• 2006

This paper analyzes a price discovery process for international crude oils including the WTI, Brent and Dubai. Error correction model is employed considering non-stationarity property of crude oil price and the contemporaneous causality is constructed by graph theory to analyze the short-term causality. The empirical analysis for January 4., 1999 to July 15., 2005 reveals that the Brent price interconnects between the WTI price and the Dubai price. This result implies the substantial influence of the Brent price as a marker oil.

• Science of Emotion and Sensibility
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• v.24 no.1
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• pp.59-72
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• 2021

Concepts and categories offer the basis for inference pertaining to unobserved features. Prior research on category-based induction that used blank properties has suggested that similarity between categories and features explains feature inference (Rips, 1975; Osherson et al., 1990). However, it was shown by later research that prior knowledge had a large influence on category-based inference and cases were reported where similarity effects completely disappeared. Thus, this study tested category-based feature inference when features are connected in a causal chain and proposed a feature inference model that predicts participants' inference ratings. Each participant learned a category with four features connected in a causal chain and then performed feature inference tasks for an unobserved feature in various exemplars of the category. The results revealed nonindependence, that is, the features not only linked directly to the target feature but also to those screened-off by other feature nodes and affected feature inference (a violation of the causal Markov condition). Feature inference model of causal model theory (Sloman, 2005) explained nonindependence by predicting the effects of directly linked features and indirectly related features. Indirect features equally affected participants' inference regardless of causal distance, and the model predicted smaller effects regarding causally distant features.

• Journal of Digital Convergence
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• v.12 no.6
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• pp.69-77
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• 2014

System Dynamics(SD) is one of the complexity theories that has attracted attention as a computer-aided simulation methodology to analyze a dynamic problem and to develop a policy(strategy) in social science. Though there are properly unproven cases in research models which were developed in various fields by SD methodology during the last five decades, they are utilized as models to represent SD sub-theories. For this reason, this study targeted the population dynamics model which was frequently utilized to explain SD fundamentals and it proved errors of reasoning a structure of the existing causal and dominant feedback loop. Consequently, we presented a strategy to strengthen the coherence between CLD(causal loop diagram) and SFD(stocks-and-flows diagram) for improving validity of the existing model. The findings of this study contribute to the advancement of the existing SD and to the reinforcement of validation for policy research models of SD.

• Journal of Convergence for Information Technology
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• v.9 no.6
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• pp.65-74
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• 2019

The purpose of this study is to reveal the causal relationship between core categories experienced by firefighters in the organization based on the grounded theory as a method of qualitative research. In this study, we interviewed 50 firefighter information providers who work in Seoul in 2014, 2017, and 2018, and conducted a research model that shows the causal relationship of core categories through open coding, axial coding, and selective coding. As a result, compassion experienced by information providers is revealed as a core category, and this compassion has a positive effect on positive work related identity, collective self esteem, and job performance. Therefore, the theoretical implication of this study is that it has derived a research model that shows the causal relationship between compassion and job performance based on grounded theory as a qualitative research methodology. This study will contribute to the formation of the organizational culture that enables firefighters who desperately need compassion in the fire department organization to more actively exchange compassionate actions.

• Health Policy and Management
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• v.5 no.1
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• pp.161-191
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• 1995

본 논문은 서구의 이론을 바탕으로 개발된 직무만족도에 관한 인과모형을 한국의 병원근무자들을 대상으로 실증적으로 검증하고 직무만족도를 결정하는 인과적 요인들을 파악하는데 목적이 있다. 본 모형의 종속변수는 직무만족도이고 독립변수로는 3개집단의 변수들로 크게 분류하여 선정되었는데 첫째, 사회적 변수들로써 직무의 자율성, 직무의 모호성, 직무 갈등, 직무의 양, 상사 및 동료와의 관계, 발전가능성, 직무의 단순성, 분배정의, 승진기회, 물리적 근무환경, 임금 등이며, 둘째, 심리적 변수들로써 기대 충족도, 근무의욕, 적극적 심성, 부정적 심성 등이고, 셋째, 환경적 변수들로써 외부 취업기회와 가족에 대한 책임 등이 사용되었다. 또한 독립변수들이 종속변수에 영향을 미치는데 있어 개인의 가치관이 어떻게 작용하는지도 검증하였다. 자료수집을 위해 대구에 위치한 750병상 규모의 한 대학병원에 근무하는 전직원을 대상으로 자기가 입식 설문조사를 실시하였으며 879매가 회수되어 74.7%의 응답율을 기록하였다. 수집된 자료중 사용 가능한 836명의 응답을 바탕으로 선형구조관계분석(LISREL)과 다중회구분석기법을 이용하여 인과 모형을 검증하였다. 분석결과 직무만족도에 유의한 영향력을 가지는 변수들은 직무의 모호성, 직무의 모호성, 동료관계, 직무의 단순성, 분배정의(이상 시회적 변수), 기대의 총족도, 근무의욕, 적극적 심성, 부정적 심성(이상 심리적 변수), 외부 취업기회(환경적 변수) 등인 것으로 나타났으며 개인의 가치관과 인구학적인 변수들은 직무만족도에 큰 영향을 미치지 못하는 것으로 나타났다. 또한 본 연구에서 사용된 인과모형은 직무만족도의 변이를 75.4% 설명함으로서 기존의 다른 모형들 보다 높은 설명력을 나타내었다. 결론적으로 본 연구에서 사용된 직무만족도의 인과모형은 한국의 조직에서도 적용이 가능한 것으로 보이며, 또한 직무만족도의 일반적 모형은 구조적 변수, 심리적 변수, 그리고 환경적 변수들을 모두 포함하는 포괄적인 모형이 타당하다는 결론을 얻을 수 있었다.

• Korean Journal of Cognitive Science
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• v.28 no.4
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• pp.329-347
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• 2017

Early research into category-based feature inference reported various phenomena in human thinking including typicality, diversity, similarity effects, etc. Later research discovered that participants' prior knowledge has an extensive influence on these sorts of reasoning. The current research tested the effects of causal knowledge on feature inference and conducted modeling on the results. Participants performed feature inference for categories consisted of four features where the features were connected either in common cause or common effect structure. The results showed typicality effects along with violations of causal Markov condition in common cause structure and causal discounting in common effect structure. To model the results, it was assumed that participants perform feature inference based on the difference between the probabilities of an exemplar with the target feature and an exemplar without the target feature (that is, $p(E_{F(X)}{\mid}Cat)-p(E_{F({\sim}X)}{\mid}Cat)$). Exemplar probabilities were computed based on causal model theory (Rehder, 2003) and applied to inference for target features. The results showed that the model predicts not only typicality effects but also violations of causal Markov condition and causal discounting observed in participants' data.

• Journal of Korean Society of Transportation
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• v.22 no.2 s.73
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• pp.77-86
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• 2004

In this study, we applied the Bayesian Network for the case of the mode choice models using the Seoul metropolitan area's house trip survey Data. Sex and age were used lot the independent variables for the explanation or the mode choice, and the relationships between the mode choice and the travellers' social characteristics were identified by the Bayesian Network. Furthermore, trip and mode's characteristics such as time and fare were also used for independent variables and the mode choice models were developed. It was found that the Bayesian Network were useful tool to overcome the problems which were in the traditional mode choice models. In particular, the various transport policies could be evaluated in the very short time by the established relation-ships. It is expected that the Bayesian Network will be utilized as the important tools for the transport analysis.

• Korean Journal of Logic
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• v.22 no.1
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• pp.183-209
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• 2019

In this paper, I first examine Reiss'(2005) arguments for the causal instrumental variable. Second, I argue that the conditions for causal transitivity I consider meet what the causal instrumental variables and the interveners of the manipulation theory of causation are intended to hold. Reiss shows that two conditions for instrumental variables are not sufficient for causal significance of independent variables for dependent variables. Reiss articulates and reformulates the conditions for instrumental variables in terms of the conditions on causality, while naming his instrumental variables as causal instrumental variables. Reiss argues that the causal instrumental variables are similar to the interveners of the manipulation, or intervention theory of causation. He further argues that the causal instrumental variables do a better job the interveners do. I argue that the conditions for causal transitivity I consider meet the goal the conditions for the causal instrumental variables and the conditions for the interveners both are intended to achieve.

• KDI Journal of Economic Policy
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• v.13 no.4
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• pp.117-140
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• 1991

The purpose of this paper is primarily to introduce a nonparametric statistical tool developed by Baek and Brock to detect a unidirectional causal ordering between two economic variables and apply it to interesting macroeconomic relationships among money, production and prices. It can be applied to any other causal structure, for instance, defense spending and economic performance, stock market index and market interest rates etc. A key building block of the test for nonlinear Granger causality used in this paper is the correlation. The main emphasis is put on nonlinear causal structure rather than a linear one because the conventional F-test provides high power against the linear causal relationship. Based on asymptotic normality of our test statistic, the nonlinear causality test is finally derived. Size of the test is reported for some parameters. When it is applied to a money, production and prices model, some evidences of nonlinear causality are found by the corrected size of the test. For instance, nonlinear causal relationships between production and prices are demonstrated in both directions, however, these results were ignored by the conventional F-test. A similar results between money and prices are obtained at high lag variables.