• 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'.

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

• Annual Conference on Human and Language Technology
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• 1997.10a
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• pp.375-380
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• 1997

"모든 사람은 죽는다"라는 믿음을 내용으로 하는 마음의 상태가 "그 사람도 죽는다"라는 믿음을 내용으로 하는 마음의 상태를 인과적으로 야기하는가? 두 믿음 사이의 미시적 얼개가 인과 관계를 골자로 하고 있다는 사실에서 그 미시적 얼개에 수반하는 거시적 얼개도 인과 관계라는 사실이 함축되는가? 거시적 얼개를 물리적 관점에서 보자면 두 믿음 사이의 관계는 인과적이다. 그러나 거시적 얼개의 포인트는 미시적 얼개와는 달리 물리적 인과성에만 맞춰져 있지 않다. 한 믿음에서 다른 믿음으로의 이행에서 그 포인트는 믿음의 신경생리학적 토대가 아니라 믿음의 의미에 있다. 따라서 양자 사이의 이행 관계는 추론이나 바램 등과 같은 인간의 해석적 의미 연관의 관점에서 서술되는 것이 더 자연스럽고 이치에 맞는다.

• Proceedings of the Korean Society for Cognitive Science Conference
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• 2006.06a
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• pp.125-130
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• 2006

원활한 의사소통을 위해서는 문장들을 연결하여 흐름을 조직하고 말로 산출하고 전체적인 의미를 파악할 수 있어야 한다. 이야기는 이러한 문장들이 연결되어있는 것으로, 종속적이거나 나열적인 이야기 특성은 의사소통장애인의 이야기 이해와 산출의 수행에 영향을 미칠 수 있다. 본 연구에서는 이야기 특성에 따른 유창성 실어증환자의 이야기 이해 및 산출의 능력을 알아보고, 이해과제 수행이 산출과제에 미치는 영향을 분석해보았다. 이야기 종류로는 시간적 나열 이야기와 인과적 관계 이야기, 유머가 있는 이야기를 사용하였으며, 사실적 정보, 텍스트 추론, 빠진 정보추론 등 세 가지의 이해과제를 통하여 이해 능력을 측정하였다. 산출능력은 이해과제 전과 후에 CIU 비율로 질적인 측면을 측정하고, 분당어절 수를 이용하여 양적인 측면을 분석하였다. 그 결과 이해측면은 세 가지 이야기 모두 사실적 정보에 대한 이해 능력이 상대적으로 좋았으며, 오류의 형태는 추론오류가 가장 많이 나타났다. 산출에서는 인과적 관계이야기에서의 CIU 비율이 가장 높았고, 이해과제 전, 후의 차이를 비교한 결과, CIU 비율은 변화하지 않았으나, 분당 어절수에서는 증가하고 있음을 보여주었다. 이야기의 종류에 따라서 유창성 실어증화자의 산출과제의 수행수준은 다르게 나타났다. 그리고 이해과제의 수행이 산출과제에서 양적인 증가는 가져왔으나 질적인 수준에는 아무런 영향을 미치지 않았다.

• Journal of The Korean Association For Science Education
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• v.38 no.5
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• pp.635-647
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• 2018

Motivated reasoning refers to biased reasoning that is affected by motivation to achieve a particular result or goal. In this study, we attempted a theoretical study on motivated reasoning that hinders the development of scientific thinking and empirical study on actual context of motivated reasoning in the research experiences of next-generation Korean researchers in the field of science and technology. To be specific, literature reviews were conducted to explore the psychological meaning of motivated reasoning and its negative impact on scientific thinking and science research. To understand the substantial meaning and context of motivated reasoning in the field of real science and technology research, we conducted in-depth interviews with eight graduate students and one young science and technology researcher. As a result of the literature reviews, we found out that motivated reasoning can interfere with the proper theory and data coordination, which is the core process of scientific thinking at the individual level. At the socio-cultural level, it can lead to cessation of constructing scientific knowledge and it can act as a mechanism in the process of using science for specific socio-cultural beliefs or purposes, thereby hindering the development of science and technology based on rationale and objective scientific thinking. Quantitative analysis with in-depth interview data showed that graduate students and the young researcher's experienced motivated reasoning results in trying to protect prior beliefs, make hasty conclusions, protecting socio-cultural belief or rationalizing decisions made by their community. Their motivated reasoning could become an obstacle in constructing valid science and technology knowledge through appropriate theory and evidence coordination. Based on these findings we discussed science education for improving scientific thinking.

• Journal of Korean Philosophical Society
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• v.129
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• pp.267-290
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• 2014

The slippery slope argument means that if we accept a type of action A, we are committed to accepting B, C and eventually N. Then, N is situation which we must not accept morally. It works causal mechnism that B because A is raised, C because B is raised. But in the logic textbooks and treatises, the slippery slope argument is classified as fallacy. The reason is that the argument is not a causal argument. Actually, it is a probable. Also it is argued that the argument is wrong because it fears about the future extremely. But We can not say all slippery slope argument is fallacy even though a slippery slope argument is sometimes fallacy. I think it is persuasive argument in a significant place. Therefore I argue that the argument is not simple logic as a form of thinking, but practical reasoning applied the context of dialogue. So in order to find it to be practical reasoning we demand the new understanding to fallacy theory. In traditionally, fallacy is defined to wrong reasoning logically, but according to Walton, fallacy means a verbal tactic or deceptive trick that can be used to cause someone to fall down in argument. That is to say, whether or not the argument is successful depends on how it uses as argument tactic in a given context of dialogue. Therefore I argue that whether or not the argument is successful, because of it is practical problem used in a context of dialogue, is to be approached to pragma and dialectical method, not semantic.

• Korean Journal of Cognitive Science
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• v.24 no.4
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• pp.323-338
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• 2013

There are multiple process mechanism in causal reasoning, which is estimation of the causal strength between cause and result. Further, because these mechanisms operate on different time phase during causal reasoning, it is highly possible that different individual difference factors are related to individual mechanisms of causal reasoning. Especially, the phenomena of conditionalization and discounting reflect attention to multiple potential causes when people infer the relationship between cause and effect. In this study, we manipulated self-construal which is an individual difference factor that reflects context sensitivity in cognition. As results, no difference was observed in conditionalization between individuals with an independent self-construal and those with an interdependent self-construal. However, independent self-construal group was observed to be lower in discounting than the interdependent self-construal group. The results indicate that conditionalization and discounting are independent cognitive process in human causal reasoning.

• The Korean Journal of Applied Statistics
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• v.34 no.5
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• pp.837-848
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• 2021

Evaluating or predicting the effectiveness of economic policies is an important issue, but it is difficult to find an economic variable which causes a significant result because there are numerous variables that cannot be taken into account. A randomized controlled experiment is the best way to investigate causality, but it is not realistically possible to control through randomization and intervention in time series data such as macroeconomic data. Although some analysis methods have been proposed to find causality, the methods such as Granger causality method and Chow test are insufficient to explain causality. Recently, Pfister et al. (2019) proposed invariant causal prediction methods which can be applicable in time series data. In this paper, we introduce the method of Pfister et al. (2019) and use the method to find macroeconomic variables invariantly affecting the won-dollar exchange rate.

• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.10 no.1
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• pp.46-55
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• 1996

본 논문에서는 변전소의 변전설비에 대한 고장진단을 위한 전문가 시스템을 개발하였다. 제안된 전문가 시스템에서는 변전소의 구조적 특성을 효과적으로 이용하기 위하여 두 종류의 새로운 자료 구조를 제안하였다. 먼저 설비 연결자료로, 이는 변전소의 수전단에서 배전단으로 이어지는 계층적 구조를 이용하여 소내 설비들의 전기적 연결상태 인식을 효과적으로 수행할 수 있도록 한다. 다음으로, 각 보호 계전기의 보호 영역 자료를 제안하였는데, 이것은 전문가 시스템 가동시에 자동으로 구성되면, 보호계전기의 주보호 설비 뿐만 아니라 후비보호와 2차 후비보호 등의 설비들을 탐색하여 자료구조에 포함함으로써 추론의 효율을 높였다. 본 전문가 시스템에서는 2단계 추론을 수행하는데, 1단계에서는 설비 연결자료와 보호 영역 자료를 이용하여 고장 후보들을 선정하고 2단계에서는 보호기기 동작간의 인과관계를 이용하여 고장 위치를 파악하고 동작한 보호기기들에 대한 설명을 하도록 하였다. 제안한 전문가 시스템은 실제 154[kV]급 변전소 모형에 적용하여 도출된 결과의 타당성과 수행시간의 실효성을 보였다.

• Science of Emotion and Sensibility
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• v.17 no.3
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• pp.39-48
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• 2014

Good and bad luck is an important factor that frequently affects human information processing. However, in spite of its significance, few studies have been done to examine how good and bad luck influences information processing and reasoning. The current research was performed to explore the effect of good and bad luck on reasoning and, for this, two experiments were conducted. In experiment 1, participants were primed with good or bad luck and were asked to make an inference for a given murder case and include as many as clues for it, while in experiment 2, participants were asked to exclude as many as clues for the same murder case. Results show that, in experiment 1, participants who were primed with good luck included more clues than those who were primed with bad luck. However, in Experiment 2, it was found that participants who were primed with bad luck excluded more clues than those who were primed with good luck. Findings from this study indicate that priming good luck enhances holistic thinking which leads to including more and excluding less clues whereas priming bad luck increases analytic thinking which leads to including less and excluding more clues. Implications of this study for inference and decision making, consumer behavior, and addict psychology are discussed.