• Science of Emotion and Sensibility
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• v.26 no.1
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• pp.55-64
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• 2023

This research investigated category-based feature inference when category features were connected in common cause and common effect causal networks. Previous studies that tested feature inference in causal categories showed unique inference patterns depending on causal direction, number of related features, whether the to-be-inferred feature was cause or effect, etc. However, these prior studies primarily focused on inference pattens that arise from causal relations, and few studies directly explored how the effects of causal relations vary depending on causal strength. We tested feature inference in common cause (Expt. 1) and common effect (Expt. 2) causal categories when casual strengths were either strong or weak. To this end, we had participants learn causal categories where features were causally linked and then perform feature inference task. The results showed that causal strengths as well as causal relations had important impacts on feature inference. When causal strength was strong, inference for common cause feature became weaker but that for the common effect feature became stronger. Moreover, when causal strength was strong and common cause was present, inference for the effect features became stronger, whereas the results were reversed in common effect networks. In particular, in common effect networks, casual discounting was more evident with strong causal strength. These results consistently demonstrate that participants consider not only causal relations but also causal strength in feature inference of causal categories.

• 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.

• Korean System Dynamics Review
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• v.6 no.1
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• pp.5-15
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• 2005

This paper investigates psychological differences between constructors and interpreters of causal maps. This paper argues that dissipation effects and dilution effects applies to those who are to interpret causal maps not to those who construct them. Dissipation effects are psychological tendency that people perceive causal effect as weak as the number of causal links increases. Dilution effects occur when people undervalue the strength of causal relation as the number of causal variables increases. Experimental results show that concentration effects opposite to the dissipation effects and dilution effects explain more correctly the perception of constructors of causal maps. This paper points out that this asymmetric psychological tendencies between constructors and interpreters of causal maps is the psychological source of the communication problems between systems thinkers and their clients.

• 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.5 no.2
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• pp.115-131
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• 2002

The problem of disjunctive causal factors is generalized as follows. Suppose that there are no factors of the kind considered so far that need to be held fixed in background contexts. Nevertheless, it is still possible that within the background contexts, each disjunct of a disjunctive causal factor X v W confers a different probability on an effect factor in Question. So a problem arises of how we identify a single causally significant probability of the effect factor in the presence of the disjunctive causal factor, assuming that each disjunct of the disjunctive causal factor confers a different probability on the effect factor. In this paper, I first introduce an experiment in which disjunctive causal factors seem to pose a problem for the theory of probabilistic causation. Second, I show how Eells' solution to the problem of disjunctive causal factors meets the problem that arises in the experiment. Third, I examine Hitchcock's arguments against Eells' solution, arguing that Hitchcock misconstrues Eells' solution, and disregards the feature of the theory of probabilistic causation such that a factor is a causal factor for another factor relative to a population P of a population type Q.

• Journal of Preventive Medicine and Public Health
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• v.56 no.4
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• pp.303-311
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• 2023

Traditional mediation analysis, which relies on linear regression models, has faced criticism due to its limited suitability for cases involving different types of variables and complex covariates, such as interactions. This can result in unclear definitions of direct and indirect effects. As an alternative, causal mediation analysis using the counterfactual framework has been introduced to provide clearer definitions of direct and indirect effects while allowing for more flexible modeling methods. However, the conceptual understanding of this approach based on the counterfactual framework remains challenging for applied researchers. To address this issue, the present article was written to highlight and illustrate the definitions of causal estimands, including controlled direct effect, natural direct effect, and natural indirect effect, based on the key concept of nested counterfactuals. Furthermore, we recommend using 2 R packages, 'medflex' and 'mediation', to perform causal mediation analysis and provide public health examples. The article also offers caveats and guidelines for accurate interpretation of the results.

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

• Health Policy and Management
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• v.8 no.1
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• pp.52-96
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• 1998

To find causal factors and improvement plans of organizational commitment of public servants in urban health centers, a hypothetical causal model, which included 2 endogenous variables(organizational commitment & organizational satisfaction) and 15 exogenous variables, was constructed. Exogenous variables consisted of individual factors (sex, age, education, job-grade, and annual salary), psychological variables(pride for organization, extrinsic motivation, intrinsic motivation and support of supervisor) ad structural variables(formalization, centralization, communication, job-conflict, job-decision, and workload). In the hypothetical causal model, organizational commitment was supposed to be effect variable, and organizational satisfaction was presumed to be intervening variable to mediate between organizational commitment and exogenous variables. For data collection, cross-sectional self-administered questionnaire survey was conducted to 1,295 public servants from 32 urban health centers nationwide. The survey responses were from 934, 72.1% of subjects. But 756 responses(58.4%) were analyzed because of excluding ones with missing values. The hypothetical causal model was fitted by covariance structural analysis with maximum likelihood method. Main results were as follows: (1) The fitted causal model accounted for 33 and 55 percent of total variance of organizational commitment and organizational satisfaction of public servants, respectively. (2) In order of effect size, pride for organization, supervisor support, communication, extrinsic motivation and centralization had an indirect effect effect on organizational commitment through organizational satisfaction. However, the effect of centralization was negative. (3) Pride for organiztion, intrinsic motivation, organizational satisfaction, job-conflict, supervisor support, communication, age, centralization, annual salar and extrinsic motivation had indirect or direct effects on organizational commitment in order of effect size. Among them, effects of job-conflict and centraldization were negative. In conclusion, these results suggested that organizational commitment of public servants in urban health centers could be enhanced by pride for organization, intrinsic and extrinsic motivations, prevention of job-conflict and excess centralization, supervisor support and active communication. Especially, pride for organization and intrinsic motivation were expected to play the most important role.

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