• 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.

• Journal of the Ergonomics Society of Korea
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• v.22 no.2
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• pp.1-13
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• 2003

In general, causal relationship for theoretical concepts is hypothesized based on precedent studies and tested by a structural equation model. However, when theoretical backgrounds are scarce or absent, the causal relationship is hypothesized operatively by the purpose and scope of research and tested by overall goodness-of-fit indices such as GFI and RMR. Such a causal relationship can't be most appropriate statistically because it is selected as specific relationship from researcher's view among possible causal relationships. Therefore, this study is to propose a procedure for identifying the causal relationship that produces the best GFI among possible causal relationships for theoretical concepts.

• The Korean Society of Law and Medicine
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• v.15 no.2
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• pp.435-459
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• 2014

Supreme Court of Korea has been mitigating the burden of proof on the malpractice and causal relation by a patient in accordance with the practical transfer of such burden of proof on causal relation as well as relieving a doctor's burden of proof on mistake in the civil damage claim suits on the malpractice. However, a prosecutor shall strictly prove the causal relation between malpractice and unfavorable results as well as a doctor's mistake in the criminal cases for making a doctor accept the professional negligence resulting in death or injury in accordance with In Dubio Pro Reo principles. Furthermore, it shall not be allowed to relieve the burden of proof on malpractice and causal relation which has been frequently applied in the civil proceedings. Nevertheless, it was widely known that the front-line courts accepted the malpractice and causal relation by quoting the legal principles on relieving the burden of proof on malpractice and causal relation applied in the civil cases even in criminal cases with no or insufficient proof on malpractice or causal relation. However, the latest precedents in Supreme Court explicitly declared the opinion that there was no reason to apply the legal principle to relieve the burden of proof on the malpractice and causal relation in the criminal cases requiring the proof 'which doesn't cause any reasonable doubt' on malpractice and causal relation in accordance with the legal principles 'favorable judgment for a defendant in case of any doubt' on the basis of the strict principle of 'nulla poena sine lege.' Accordingly, Supreme court definitely clarified that there would be no reason to relieve the burden of proof on malpractice and causal relation in criminal cases by reversing several original judgments accepting malpractice and causal relation even though there were no strict evidence.

• 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.

• Korean Journal of Logic
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• v.20 no.1
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• pp.21-67
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• 2017

In this paper, I critically examine Sartorio's (2006) argument for disjunctive cause, and put forth disjunctive causal structure in a different way. I show that the disjunctive causal structure meets not just what Sartorio means to claim but also our understanding of causal responsibility. First, I introduce Sartorio's argument for disjunctive cause. Second, I critically discuss Sartorio's responses to the criticisms of her arguments for disjunctive cause, and propose another problem with her arguments. Finally, I explicate in a different way Sartorio's disjunctive cause in terms of disjunctive causal structure founded on moderate variables, causal interaction, and probability trajectory. I notice, regarding the disjunctive causal structure, the role of causal interaction of cause events with moderate variables. I reveal, regarding the disjunctive causal structure, the significance of indetermination of cause events and effect events for our understanding of causal responsibility. I show that the disjunctive causal structure guides us more convincingly to assign causal responsibility to an agent. I come to three conclusions. First, there is no disjunctive cause event Sartorio argues for. Second, propensities of events to be causally connected to an effect event constitute disjunctive relation. Third, we should notice indetermination of cause events and effect events while assigning causal responsibility to an agent.

• 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.

• 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.

• 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.

• Journal of Korean Elementary Science Education
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• v.27 no.2
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• pp.179-188
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• 2008

The purpose of this study is to investigate the effects of the causal thinking activity of Let's Think Through Science(LTTS) program on causal thinking and science process skills of elementary school students. Four classes of 4th graders (N=119) from a elementary school were divided into the control and the experimental groups. Causal thinking activity of LTTS program was used with the experimental group, while the normal curriculum was conducted with the control group. Both groups were given a pre-post test on causal thinking abilities and science process skills. And the experimental group was given 15- item questionnaires analyzing of perception on LTTS program. This study revealed that causal thinking activity of the LTTS program were effective on the development of students' causal thinking abilities and science process skills. ANCOVA results revealed that the scores of causal thinking abilities for the experimental group significantly higher than those of the control group. In the sub-tests of the causal thinking abilities all categories were effective. And ANCOVA results of the science process skills were also effective. Science process elements of observation, recognizing of a problem were significantly higher. And elementary students preferred to the causal thinking activity of LTTS program so that it was interesting, useful, helpful to each other in studying science.

• Journal of Korean Academy of Nursing
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• v.31 no.2
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• pp.232-243
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• 2001

When people undergo stressful situations such as a cancer diagnosis, they ask, "why me\ulcorner" The causal attributions people make about cancer influence what kind of coping strategies are chosen. Weiner (1979) suggested three dimensions of causal attributions: focus of causality, stability, and controllability. The purpose of the present study was to test the relation between causal attributions and self-efficacy in patients with cancer. The subjects were 194 patients who had been diagnosed cancer one year ago and attended an outpatient clinic. 1. Each mean score of causal attribution dimensions (focus of control, stability, controllability) that each patient made about cancer was 2.47, 2.73, 2.86, 3.35, and 3.28. The mean score of self-efficacy was 71.03. 2. There was a significant negative correlation between self efficacy and controllability. Particularly, there was a significant negative relationship between self efficacy and external controllability. Based upon these results, it is recommended that the developing nursing interventions to change causal attribution and self-efficacy is necessary. A number of theoretical relationships and empirical finding are confirmed by this data, and future proposals in research is suggested.