• Korean Journal of Logic
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• v.18 no.1
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• pp.83-133
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• 2015

In this paper, I pose a problem for Hitchcock's arguments for two concepts of effects that are intended to explicate double causal effects, and put forth a theory that is intended not just to meet the problem but also to accommodate Hitchcock's theory and Eells' theory both. First, I introduce an example of dual causal effects, and examine the accounts of Otte(1985) and Eells(1987) on how to explicate the dual effects. I show that their accounts of the dual effects help us understand the problem of dual effects and see how different it is for Cartwright(1979, 1989, 1995), Eells(1991, 1995), and Hitchcock(2001a) to meet the problem. Second, I introduce two concepts of effects on Hitchcock(2001a), that is, net effect and component effect that are allegedly analogous to two effects of structural equation model. Third, I reveal the significance of homogeneous subpopulation and causal interaction regarding the problem of dual effects while examining Cartwright's theory and Elles' theory. Fourth, I critically examine the two concepts of effects on Hitchcock and argue against Hitchcock's criticism of Eells' theory. Fifth, I take a moderator variable of structural equation model and a moderator effect into the probabilistic theory of causality, and formally generalize causal interaction due to the dual effects in terms of disjunctive relation and counterfactual conditionals. I expect my account of disjunctive relation and counterfactual conditionals to contribute not just to several problems the received theories of causal modelling confront but also to the structural equation models many people exploit as a promising statistical methodology.

• Korean Journal of Cognitive Science
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• v.27 no.4
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• pp.541-572
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• 2016

Causal reasoning is actively studied not only by psychologists but, in recent years, also by cognitive scientists taking the Bayesian approach. This paper seeks to provide an overview of the recent trends in causal reasoning research with a focus on the power probabilistic contrast theory of causality, a major psychological theory on causal inference. The power probabilistic contrast theory (PPCT) assumes that a cause is a power that initiates or inhibits the result. This power is purported be understood through statistical correlation under certain conditions. The paper examines the supporting empirical evidence in the development of PPCT. Also, introduced are the theoretical dispute between the PPCT and the model based on Bayesian approach, and the current developments and implications of research on causal invariance hypothesis, which states that cause operates identically regardless of the context. Recent studies have produced experimental results that cannot be readily explained by existing empirical approach. Therefore, these results call for serious examination of the power theory of causality by researchers in neighboring fields such as philosophy, statistics, and artificial intelligence.

• Korean System Dynamics Review
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• v.10 no.1
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• pp.33-60
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• 2009

This study explores the limitation in making a causal model through an existing case and proposes an alternative plan to improve a theoretical system of causation modeling. To make a dynamic and actual model, several principles are needed such as reality based analysis of system structures and dynamics, consistent expression of causations, conversion of numerical formulas to causal relations, classification and arrangement of variables by size of concept, etc. However, it is hard to find cases to apply these considerations from existing models in System Dynamics. Therefore, this study verifies errors of derived models from literatures and proposes principles and guides that should be considered to make a sound dynamic model on a causal map. It contributes to making an opportunity for exciting public opinion to improve theory about causal maps, yet it has limitation that the study does not advance forward to the experimental step. For future study, it plans to make up by classifying and leveling causal variables, developing a dynamic BSC model.

• 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.

• 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.

• Science of Emotion and Sensibility
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• v.24 no.1
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• pp.59-72
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• 2021

Concepts and categories offer the basis for inference pertaining to unobserved features. Prior research on category-based induction that used blank properties has suggested that similarity between categories and features explains feature inference (Rips, 1975; Osherson et al., 1990). However, it was shown by later research that prior knowledge had a large influence on category-based inference and cases were reported where similarity effects completely disappeared. Thus, this study tested category-based feature inference when features are connected in a causal chain and proposed a feature inference model that predicts participants' inference ratings. Each participant learned a category with four features connected in a causal chain and then performed feature inference tasks for an unobserved feature in various exemplars of the category. The results revealed nonindependence, that is, the features not only linked directly to the target feature but also to those screened-off by other feature nodes and affected feature inference (a violation of the causal Markov condition). Feature inference model of causal model theory (Sloman, 2005) explained nonindependence by predicting the effects of directly linked features and indirectly related features. Indirect features equally affected participants' inference regardless of causal distance, and the model predicted smaller effects regarding causally distant features.

• Korean Journal of Cognitive Science
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• v.28 no.4
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• pp.329-347
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• 2017

Early research into category-based feature inference reported various phenomena in human thinking including typicality, diversity, similarity effects, etc. Later research discovered that participants' prior knowledge has an extensive influence on these sorts of reasoning. The current research tested the effects of causal knowledge on feature inference and conducted modeling on the results. Participants performed feature inference for categories consisted of four features where the features were connected either in common cause or common effect structure. The results showed typicality effects along with violations of causal Markov condition in common cause structure and causal discounting in common effect structure. To model the results, it was assumed that participants perform feature inference based on the difference between the probabilities of an exemplar with the target feature and an exemplar without the target feature (that is, $p(E_{F(X)}{\mid}Cat)-p(E_{F({\sim}X)}{\mid}Cat)$). Exemplar probabilities were computed based on causal model theory (Rehder, 2003) and applied to inference for target features. The results showed that the model predicts not only typicality effects but also violations of causal Markov condition and causal discounting observed in participants' data.

• Asia pacific journal of information systems
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• v.7 no.2
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• pp.225-266
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• 1997

A Causal Transition Model of the IT impact on organization is proposed. The model is based upon the premise that the IT impact is a multi-phase, multi-realm phenomenon, and that the IT impact in one organizational realm logically transpires to another realm, thus forming complex causal webs among them. Two exploratory research studies, the one qualitative and the other quantitative, were conductea to validate the model in a setting involving major structural reorganization of the organizations' IT function. The research results provide support for the general theory structure of the model. The findings include: ⅰ) the IT impact manifests on multiple organizational realms, with different degrees of strength, ⅱ) the impact on the realms follow a particular causal transition path among them, and ⅲ) the IT impact manifests on and through the information processing aspect of work. The results, however, indicate that people's perception of the IT impact is strongly mitigated by the IT relevance of work, and that the organization is affected as much by the structural arrangement surrounding and accompanying the IT as by the technology itself, suggesting that the IT impact is an organizational phenomenon as well as a technological phenomenon. The implications of the research results are discussed at the end.

• Journal of Korea Society of Industrial Information Systems
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• v.22 no.6
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• pp.71-84
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• 2017

Over the past decade, we have witnessed Serious Debates in MISQ and Other Journals Between Two Camps that have Differing Views on the use of Causal Indicators to Measure Constructs. There is the Camp that advocates Causal Indicators (ADVOCATE) and the Camp that opposes Their Usage (OPPONENT). The Debates have been primarily centered on the OPPONENT's Argument that the Meaning of a Latent Variable is determined by its Outcome Variables. However, Little Effort has been made to Validate the ADVOCATE's Dispute (Against the OPPONENT's Arguments) that the Meaning of a Latent Variable is decided by its Causal Indicators if there is no Misspecification. Our Study precisely examines the Integrity of the Argument. For this, we empirically examine how the two Primary Psychometric Properties-Comprehensiveness and Interrelationship-of Causal Indicators Influence Theory Testing between Latent Variables through Three Different Tests (i.e., Comprehensive Test, Interrelationship Test, and Mixed Test). Conducted on Two Different Datasets, Our Analysis Consistently Reveals that Structural Path Coefficients are Hardly Sensitive to the Changes (i.e., Misspecification) in the Properties of Causal Indicators. The Discovery offers Important Evidence that the Sound Theoretical Logic of a Causal Model is not in Sync with the Empirical Mechanism of Parameter Estimation. This Underscores that a Latent Variable Formed by Causal Indicators is empirically an elusive notion that is Difficult to Operationalize. As Our Results have Significant Implications on the Integrity of Numerous IS studies which have conducted Theory or Hypothesis Testing Using Causal Indicators, we strongly advocate Open Discussions among Methodologists regarding Our Findings and Their Implications for Both Published IS Research and Future Practices.

• Journal of Sport and Applied Science
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• v.6 no.2
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• pp.19-25
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• 2022

Purpose: This study attempts to investigate causal and intervening conditions for sport participation of Korean immigrants in the United States. Research design, data, and methodology: Grounded theory approach was used to develop a conceptual framework that presents the psychosocial processes that occur in immigrants' experience of sport participation. Participants were selected purposefully for information-rich cases. Korean immigrants with current experience of having periodically participated in sports were the criterion for sample selection. Based on selection criteria, 9 Korean immigrants took part in interview. The interview discussions were taped and transcribed verbatim into a Word file. The process for data analysis included four grounded theory approaches of purposive and theoretical sampling, an open and axial coding, memo writing, and finally the development of the conceptual framework. Results: Six concepts were revealed in the causal conditions that facilitate the process of immigrants' sport participation in the states: Personal experience, significant others, personality, physical environment, psychological well-being, and social connection. Three concepts were revealed as the intervening conditions that block the process of immigrants' sport participation in the states: Conflict with cultural change of organization, Pressure at workplace, and Economic constraints. Conclusions: Conceptual model presents causal and intervening factors. Further implications were discussed.