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

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

• The Korean Journal of Financial Management
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• v.27 no.1
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• pp.1-30
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• 2010

This paper tested the lead-lag relationship as well as the symmetric and asymmetric volatility spillover effects between international currency futures markets and cash markets. We use five kinds of currency spot and futures markets such as British pound, Australian and Canadian dollar, Brasilian Real and won/dollar spot and futures markets. daily closing prices covering from September 15, 2003 to July 30, 2009. For this purpose we employed dynamic time series models such as the Granger causality based on VAR and time-varying MA(1)-GJR-GARCH(1, 1)-M. The main empirical results are as follows; First, according to Granger causality test, we find that the bilateral lead-lag relationship between the five countries' currency spot and futures market. The price discover effect from currency futures markets to spot market is relatively stronger than that from currency spot to futures markets. Second, based on the time varying GARCH model, we find that there is a bilateral conditional mean spillover effects between the five currency spot and futures markets. Third, we also find that there is a bilateral asymmetric volatility spillover effects between British pound, Canadian dollar, Brasilian Real and won/dollar spot and futures market. However there is a unilateral asymmetric volatility spillover effect from Australian dollar futures to cash market, not vice versa. From these empirical results we infer that most of currency futures markets have a much better price discovery function than currency cash market and are inefficient to the information.

• Management & Information Systems Review
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• v.32 no.3
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• pp.1-23
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• 2013

In this study, we try to investigate the spillover effects of volatility in international tourists arrivals between Korea and US, Japan, China by using the multivariate BEKK model from January 2005 to January 2013. In the results of this study, after the global financial crisis, we found a cointegration relationship and tourist arrivals of Japan were adjusted to recovery in the short term. Also tourists arrivals from China and Japan showed the long-term elasticity. In the conditional mean equation of a BEKK model, there were the spillover effects. And in the conditional variance equation, ARCH(${\epsilon}^2_t$) coefficients showed a strong influence on the arrivals of their own and the spillover effects and the asymmetric effects on the volatility of China and Japan arrivals. In GARCH(${\sigma}^2_t$) coefficients showed the asymmetric effects and the spillover effects of the conditional volatility among source arrivals. Therefore, we examined the asymmetric reaction of one-way or two-way tourist arrivals between source countries and Korea and the spillover effects related to tourists arrivals of source countries to Korea. We has confirmed a causal relationship between some of the tourists arrivals from source countries to korea.

• The Korean Journal of Applied Statistics
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• v.36 no.4
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• pp.323-335
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• 2023

In causal analysis of high dimensional data, it is important to reduce the dimension of covariates and transform them appropriately to control confounders that affect treatment and potential outcomes. The augmented inverse probability weighting (AIPW) method is mainly used for estimation of average treatment effect (ATE). AIPW estimator can be obtained by using estimated propensity score and outcome model. ATE estimator can be inconsistent or have large asymptotic variance when using estimated propensity score and outcome model obtained by parametric methods that includes all covariates, especially for high dimensional data. For this reason, an ATE estimation using an appropriate dimension reduction method and semiparametric model for high dimensional data is attracting attention. Semiparametric method or sparse sufficient dimensionality reduction method can be uesd for dimension reduction for the estimation of propensity score and outcome model. Recently, another method has been proposed that does not use propensity score and outcome regression. After reducing dimension of covariates, ATE estimation can be performed using matching. Among the studies on ATE estimation methods for high dimensional data, four recently proposed studies will be introduced, and how to interpret the estimated ATE will be discussed.

• The Journal of the Korea Contents Association
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• v.15 no.10
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• pp.150-159
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• 2015

This study attempted to test the scale effect of MAUP, particularly focusing on the spatial autocorrelation of sex crime, correlations among neighborhood structural variables, and causal mechanism leading to sex crime. Analysis results of nation-wide Eup-Myon-Dong and Si-Gun-Gu data discovered that the spatial autocorrelation, correlations among independent variables, and determinant coefficient of multiple regression of Si-Gun-Gu level were generally bigger and stronger than those of Eup-Myon-Dong, which appeared to be due to the averaging effect. Regarding the causal effect to sex crime, two interesting results were found: First, the ratio of non-apartment residency lowered sex crime at both levels contrary to the hypothesis. Second, the ratio of food and lodging increased sex crime only at Eup-Myon-Dong level. These suggested that future research need to perform more detailed analyses dividing data into subsets such as urban vs. rural and/or economically advantaged vs. disadvantaged areas.

• Journal of Preventive Medicine and Public Health
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• v.34 no.2
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• pp.109-118
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• 2001

The purpose of this review is to discuss the debate concerning the interpretation of epidemiologic studies on particles and health effects. Study of the 1952 air pollution disaster in London established that very high levels of particulate-based smog can cause dramatic increases in daily mortality. However, recent epidemiologic studies have reported statistically significant health effects and mortality due to low levels of air pollution. The statistical significance does not prove causation in observational studies; therefore it is necessary to evaluate these associations. There are arguments for and against each of the numerous studies using Hill's criteria, however the body of accepted evidence supports the causal association. In particular, a high level of consistency in the estimated effect of PM10 has been observed across studies worldwide. The mechanism of the relationship between air pollution and health effects is not obvious. The mechanism of particle-induced injury may involve the production of an inflammatory response by the particulate. The harvesting and the threshold effect are also major concerns regarding the health effects of air pollution. However, current epidemiologic findings indicate that linear models lacking a threshold are appropriate for assessing the effect of particulate air pollution on daily mortality even at current levels.

• Proceedings of the KAIS Fall Conference
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• 2009.12a
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• pp.574-577
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• 2009

본 논문은 외판원 문제(TSP: Traveling Salesman Problem)를 이용하여 로봇중심의 FMC(Flexible Manufacturing Cell)에서 반송 로봇의 작업순서를 설계하는 방법을 제시하였다. 이를 위해, 먼저 다수의 설비와 반송 로봇으로 구성된 대표적인 로봇 중심의 FMC를 가상으로 설계한 후, 실험계획법을 이용하여 다양한 조건에서의 주요 반응변수들의 인과관계를 규명하였다. 실험결과, 처리량, 반송로봇의가동률, 그리고 Buffer의 평균 대기 작업물의 수가 주요 반응변수들로 선정되었으며, 이를 기반으로 순서기반 조합최적화 문제인 TSP로 로봇 작업순서를 설계하였다. 제안한 방법과 기존의 방법을 비교하기 위해서 시뮬레이션을 수행 한 결과 제안된 TSP 방법이 기존의 방법 보다 반송 로봇의 교착 (Deadlock) 상태를 방지하여 처리량 등 주요 반응변수들 모두를 향상 시키는 결과를 가져왔다. 더불어,이 방법은 본 연구에서 제시한 FMC 뿐 아니라 반도체나 LCD(Liquid Crystal Display) 생산 공정과 같이 반송 로봇에 의해 구성되어 있는 장치 산업분야에 적용가능하다는 측면에서 큰 효과가 기대된다.

• Korean Journal of Cognitive Science
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• v.17 no.2
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• pp.75-102
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• 2006

Three experiments were conducted using a verification task to examine good and poor readers' generation of causal inferences(with because sentences) and contrastive inferences(with although sentences). The unfamiliar, critical verification statement was either explicitly mentioned or was implied. In Experiment 1, both good and poor readers responded accurately to the critical statement, suggesting that both groups had the linguistic knowledge necessary to the required inferences. Differences were found, however, in the groups' verification latencies. Poor, but not good, readers responded faster to explicit than to implicit verification statements for both because and although sentences. In Experiment 2, poor readers were induced to generate causal inferences for the because experimental sentences by including fillers that were apparently counterfactual unless a causal inference was made. In Experiment 3, poor readers were induced to generate contrastive inferences for the although sentences by including fillers that could only be resolved by making a contrastive inference. Verification latencies for the critical statements showed that poor readers made causal inferences in Experiment 2 and contrastive inferences in Experiment 3 doting comprehension. These results were discussed in terms of context effect: Specific encoding operations performed on anomaly backgrounded in another passage would form part of the context that guides the ongoing activity in processing potentially relevant subsequent text.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.4
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• pp.58-66
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• 2017

This study was a descriptive correlational study investigating the mediating effects of resilience in the relationship between depression and rehabilitation motivation in middle-aged stroke patients. There was a total of 185 middle-aged patients aged 40 to 64 years, who were diagnosed with stroke at a university hospital and rehabilitated at three local hospitals located in S city and four hospitals in U city. The data were collected using a self-reported questionnaire between the 1st and 31st of December in 2016. The data were analyzed using t-test, ANOVA, Pearson's correlation coefficient, and step-wise causal method using SPSS/WIN 22.0 statistical program. To test the statistical significance of the mediation effect, PROCESS and bootstrapping were used. The mean age of the subjects were $56.26{\pm}6.37$ years. There were 70.3% male subjects, an average depression level was $21.21{\pm}7.09$, an average resilience was $25.52{\pm}9.63$, and rehabilitation motivation was $47.44{\pm}5.87$. Depression was negatively related to resilience and rehabilitation motivation. However, resilience and rehabilitation motivation have a static correlation. These results confirmed that resilience appears to be a complete mediating effect in the relationship between depression and rehabilitation motivation. Therefore, it is important to develop a resilience enhancement program to improve the motivation of rehabilitation for stroke patients.