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

• The Korean Journal of Applied Statistics
• /
• v.30 no.5
• /
• pp.615-632
• /
• 2017

Unlike randomized trial, statistical strategies for inferring the unbiased causal relationship are required in the observational studies. The matching with the propensity score is one of the most popular methods to control the confounders in order to evaluate the effect of the treatment on the outcome variable. Recently, new methods for the causal inference in latent class analysis (LCA) have been proposed to estimate the average causal effect (ACE) of the treatment on the latent discrete variable. They have focused on the application study for the real dataset to estimate the ACE in LCA. In practice, however, the true values of the ACE are not known, and it is difficult to evaluate the performance of the estimated the ACE. In this study, we propose a method to generate a synthetic data using the propensity score in the framework of LCA, where treatment and outcome variables are latent. We then propose a new method for estimating the ACE in LCA and evaluate its performance via simulation studies. Furthermore we present an empirical analysis based on data form the 'National Longitudinal Study of Adolescents Health,' where puberty as a latent treatment and substance use as a latent outcome variable.

• Korean Journal of Child Studies
• /
• v.29 no.5
• /
• pp.243-269
• /
• 2008

The role of domain-specific causal mechanism information and domain-general conditional probability in young children's causal reasoning on physics and psychology was investigated with the participation of 121 3-year-olds and 121 4-year-olds recruited from seven child care centers in Seoul, Kyonggi Province, and Busan. Children watched moving pictures on physical and psychological phenomena, and were asked to choose an appropriate cause and justify their choice. Results showed that young children's causal reasoning differed depending on domain-specific mechanism. In addition, their causal reasoning on physics and psychology differed by the developmental level of causal mechanism. The interaction of domain-specific mechanism and domain-general conditional probability influenced children's causal reasoning : evident conditional probability between domain-appropriate cause and effect helped children make more inferences based on domain-specific causal mechanism.

• Journal of The Korean Association For Science Education
• /
• v.38 no.5
• /
• pp.635-647
• /
• 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 the Korean earth science society
• /
• v.28 no.6
• /
• pp.655-672
• /
• 2007

The purpose of this study was to investigate information sources and types of reasoning that non-astronomy major undergraduate students used to build their mental models on the mechanism of the Moon craters formation. In-depth interview was used to collect qualitative data, and the questions for the interview were developed through an analytical induction method. We interviewed four students individually by using Seidman's interview step. The findings revealed that the participants built nonscientific mental models, and yet they held a consistent explanatory framework. The students explained that the crater was made by the fall of a meteorite. They all suggested a similar shape of meteorite even though their drawings about the shape of craters and its related to variables were different from one another. The information sources that the participants used fur their explanatory frameworks were varied, i.e., daily experiences, subject knowledges, and intuition. In addition, they used causal reasoning, intuitional reasoning, knowledge based reasoning, and analogical reasoning.

• Journal of Families and Better Life
• /
• v.26 no.5
• /
• pp.333-354
• /
• 2008

This study aimed to investigate the role of domain-specific causal mechanism information and domain-general conditional probability in young children's causal reasoning on psychology and biology. Participants were 121 3-year-olds and 121 4-year-olds recruited from seven childcare centers in Seoul, Kyonggi Province, and Busan. After participants watched moving pictures on psychological and biological phenomena, they were asked to choose appropriate cause and justify their choices. Results of this study were as follows: First, young children made different inferences according to domain-specific causal mechanisms. Second, the developmental level of causal mechanisms has a gap between psychology and biology, and biological knowledge was proved to be separate from psychological knowledge during the preschool period. Third, young children's causal reasoning was different depending on the interaction effect of domain-specific mechanisms and domain-general conditional probability: children could make more inferences based on domain-specific causal mechanisms if conditional probability between domain-appropriate cause and effect was evident. To conclude, it can be inferred that the role of domain-specific causal mechanisms and domain-general conditional probability is not competitive but complementary in young children's causal reasoning.

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

• Environmental Analysis Health and Toxicology
• /
• v.20 no.1
• /
• pp.1-11
• /
• 2005

환경오염의 조기 경보 시스템으로 생체지표를 이용한 생태독성 모니터링이 최근 널리 연구되고 있다. 환경내의 생물종에서 측정한 생체지표를 이용한 환경 모니터링은 생태계 수준의 영향에 대한 예측 정보를 제공해 줄 수도 있다. 이를 위해서는 생체지표와 개체군 수준에서의 반응과의 인과관계가 밝혀져야 한다. 오염물질에 대한 생체의 반응은 분자, 세포, 생화학, 생리적, 개체, 개체군, 군집 수준에서 나타나게 되며, 이러한 각 단계별 반응은 반응 시간의 규모와 독성학적, 생태적 관련성에 따라 구분 지어 볼 수 있다. 각 개별 수준에서의 반응을 종합하면 오염물질에 노출된 생체의 전제적인 영향을 이해할 수 있으며, 이러한 이해를 바탕으로 개체군에서 나타나는 영향에 대한 인과관계를 추론할 수 있다. 생체지표와 개체군 수준의 반응과의 인과관계 정립은 효율적인 환경오염 예방기능 수행에 필수적인 과정이며, 다단계 생체 지표는 각 단계별 반응의 인과관계를 밝히기 위해 가장 적절한 접근 방법이다. 수서 무척추 생물인 Chironomus의 유충은 이러한 다단계 바이오마커 연구에 매우 적절한 생물학적 모델이다. 이 논문의 첫 번째 부분은 생체지표를 이용한 환경 모니터링을, 두 번째 부분은 Chironomus의 유충에서 생체지표의 적용에 대해서 다룬다.

• Journal of Korean Philosophical Society
• /
• v.147
• /
• pp.215-237
• /
• 2018

The purpose of this study is to illuminate the precise nature and the central line of Kant's proof of the causal principle stated in the Second Analogy of the 2nd. edition of the Critique of Pure Reason. The study argues for the following thesis: 1. The proof of the Second Analogy concerns only the causal principle called the "every-event-some-cause" principle, and not the causal law(s) called the "same-cause-same-event" principle. 2. The goal of the proof is to establish the possibility of knowledge of an temporal order of successive states of an object. 3. The proof is broadly an single transcendental argument in two steps. The 1st. step is an analytic argument that infers from the given perceptions of an oder of successive states of an objects to the conclusion that the causal principle is the necessary condition for the objectivity of dies perceived order. The 2nd. step is a synthetic argument that infers from the formal nature of time to the conclusion that the causal principle is a necessary condition for die possibility of objective alterations and of empirical knowledge of these alterations. 4. The poof involves not the 'non sequitur' assumed by P. F. Strawson, that is, Kant infers not directly from a feature of our perceptions to a conclusion regarding the causal relations of distinct states of affairs that supposedly correspond to these perceptions.

• Journal of KIISE:Software and Applications
• /
• v.34 no.6
• /
• pp.549-561
• /
• 2007

Vision-based scene understanding is to infer and interpret the context of a scene based on the evidences by analyzing the images. A probabilistic approach using Bayesian networks is actively researched, which is favorable for modeling and inferencing cause-and-effects. However, it is difficult to gather meaningful evidences sufficiently and design the model by human because the real situations are dynamic and uncertain. In this paper, we propose a learning method of Bayesian network that reduces the computational complexity and enhances the accuracy by searching an efficient BN structure in spite of insufficient evidences and training data. This method represents the domain knowledge as ontology and builds an efficient hierarchical BN structure under constraint rules that come from the ontology. To evaluate the proposed method, we have collected 90 images in nine types of circumstances. The result of experiments indicates that the proposed method shows good performance in the uncertain environment in spite of few evidences and it takes less time to learn.