Inferring Causal Relations by Analogy

2017 ◽  
Author(s):  
Keith J. Holyoak ◽  
Hee Seung Lee
Keyword(s):  
Analogical Reasoning ◽  
Causal Model ◽  
Causal Knowledge ◽  
Common Pattern ◽  
Causal Relations ◽  
Analogical Inference ◽  
Psychological Evidence ◽  
Inductive Inferences ◽  
Novel Target ◽  
Constituent Elements

When two situations share a common pattern of relationships among their constituent elements, people often draw an analogy between a familiar source analog and a novel target analog. This chapter reviews major subprocesses of analogical reasoning and discusses how analogical inference is guided by causal relations. Psychological evidence suggests that analogical inference often involves constructing and then running a causal model. It also provides some examples of analogies and models that have been used as tools in science education to foster understanding of critical causal relations. A Bayesian theory of causal inference by analogy illuminates how causal knowledge, represented as causal models, can be integrated with analogical reasoning to yield inductive inferences.

Concepts as Causal Models

2017 ◽  
Author(s):  
Bob Rehder
Keyword(s):  
Causal Model ◽  
Causal Models ◽  
Causal Knowledge ◽  
Causal Relations ◽  
Inductive Inferences ◽  
Category Discovery ◽  
Model Approach

This chapter evaluates the case for treating concepts as causal models, the view that people conceive of a categories as consisting of not only features but also the causal relations that link those features. In particular, it reviews the role of causal models in category-based induction. Category-based induction consists of drawing inferences about either objects or categories; in the latter case one generalizes a feature to a category (and thus its members). How causal knowledge influences how categories are formed in the first place—causal-based category discovery—is also examined. Whereas the causal model approach provides a generally compelling account of a large variety of inductive inferences, certain key discrepancies between the theory and empirical findings are highlighted. The chapter concludes with a discussion of the new sorts of representations, tasks, and tests that should be applied to the causal model approach to concepts.

scholarly journals Reducing the Complexity of Casual Representation in Bayesian Belief Network

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 1243
Author(s):  
Yit Yin Wee ◽  
Shing Chiang Tan ◽  
KuokKwee Wee
Keyword(s):  
Causal Model ◽  
Knowledge Engineering ◽  
Joint Probability ◽  
Bayesian Belief Network ◽  
Joint Probability Distribution ◽  
Causal Knowledge ◽  
Belief Network ◽  
Track Record ◽  
Probability Table ◽  
Causal Framework

Background: Bayesian Belief Network (BBN) is a well-established causal framework that is widely adopted in various domains and has a proven track record of success in research and application areas. However, BBN has weaknesses in causal knowledge elicitation and representation. The representation of the joint probability distribution in the Conditional Probability Table (CPT) has increased the complexity and difficulty for the user either in comprehending the causal knowledge or using it as a front-end modelling tool.   Methods: This study aims to propose a simplified version of the BBN ─ Bayesian causal model, which can represent the BBN intuitively and proposes an inference method based on the simplified version of BBN. The CPT in the BBN is replaced with the causal weight in the range of[-1,+1] to indicate the causal influence between the nodes. In addition, an inferential algorithm is proposed to compute and propagate the influence in the causal model.  Results: A case study is used to validate the proposed inferential algorithm. The results show that a Bayesian causal model is able to predict and diagnose the increment and decrement as in BBN.   Conclusions: The Bayesian causal model that serves as a simplified version of BBN has shown its advantages in modelling and representation, especially from the knowledge engineering perspective.

Developing a causal model to evaluate the critical issues in reverse supply chain implementation

2018 ◽  
Vol 25 (7) ◽  
pp. 1992-2017 ◽  
Author(s):  
Kaustov Chakraborty ◽  
Sandeep Mondal ◽  
Kampan Mukherjee
Keyword(s):  
Supply Chain ◽  
Semiconductor Manufacturing ◽  
Manufacturing Industry ◽  
Causal Model ◽  
Market Competition ◽  
Reverse Supply Chain ◽  
Causal Relations ◽  
Content Type ◽  
Critical Issues ◽  
Practical Implications

Purpose Approximately, 800m tons of e-waste is generated per year in India. Reverse supply chain (RSC) is the probable strategy to cope up with the issue. Setting up a RSC process is not popular in the Indian sector. There are several factors that basically control the profitability of such kind of business. Hence, the purpose of this paper is to develop a causal model among the identified issues and sub-issues for setting up a RSC in an Indian semiconductor manufacturing industry and then evaluate the critical issues based on the causal relations. Design/methodology/approach Decision-making trial and evaluation laboratory (DEMATEL) method along fuzzy set theory is used to develop the causal framework among the identified strategical and tactical issues. According to the causal relations from DEMATEL, analytical network process is then used to identify the weights of the sub-issues. Findings The cause–effect interactions among the main issues show that legislations and regulations, market-related issues and organizational issue are the most significant strategic issues. Uncertainty in the acquisition time is the most significant tactical issue because it has a crucial impact on the quality and quantity of the used products. Based on the obtained causal relations of the main issues, it is identified that the reduction of waste, creation of new opportunity, market competition, cost reduction, change in technology and location, capacity and number of recovery facility are the major sub-issues in RSC implementation. Practical implications This study is conducted on the basis of the experts’ opinion from a semiconductor manufacturing industry, situated in the southern part of India. Therefore, this proves its practical implications. Originality/value The paper provides the detail illustration of the issues in the RSC process, and the prioritization of the issues based on the cause–effect relationships also provides some meaningful managerial insights.

Casual Modeling to Improve Customs Services in Iran

2018 ◽  
pp. 156-166
Author(s):  
Alireza Hassanzadeh ◽  
Hoda Eskandari
Keyword(s):  
Decision Making ◽  
System Dynamics ◽  
Causal Model ◽  
Influential Factors ◽  
Causal Relations ◽  
The World

In its reality, the world is more complex than we expect it to be predicated and controlled; so, by minor view to issues and suppose many phenomena to be independent, it would be impossible to understand their totality and complexity. Utilizing appropriate scientific tools can provide us with useful information to manage decision making to improve methods to carry out operations and apply resources; one of these scientific tools is system dynamics approach. Therefore, based on stages in the methodology of system dynamics and by taking experts comments, in the present study, causal relations between influential factors in improvement of customs service are identified. So, by knowing these causal relations, one can allocate resources and prioritize activities leading to influence on other factors following with the best results. Finally, after validation and having the confirmation of the experts, the researcher has presented a causal model to improve customs services.

scholarly journals Neural Correlates of Causal Confounding

2020 ◽  
Vol 32 (2) ◽  
pp. 301-314
Author(s):  
Mimi Liljeholm
Keyword(s):  
Cognitive Modeling ◽  
Causal Model ◽  
Neural Substrates ◽  
Neural Correlates ◽  
Learning Context ◽  
Causal Knowledge ◽  
Causal Induction ◽  
General Constraints ◽  
Computational Cognitive Modeling ◽  
Adults And Children

As scientists, we are keenly aware that if putative causes perfectly covary, the independent influence of neither can be discerned—a “no confounding” constraint on inference, fundamental to philosophical and statistical perspectives on causation. Intriguingly, a substantial behavioral literature suggests that naïve human reasoners, adults and children, are tacitly sensitive to causal confounding. Here, a combination of fMRI and computational cognitive modeling was used to investigate neural substrates mediating such sensitivity. While being scanned, participants observed and judged the influences of various putative causes with confounded or nonconfounded, deterministic or stochastic, influences. During judgments requiring generalization of causal knowledge from a feedback-based learning context to a transfer probe, activity in the dorsomedial pFC was better accounted for by a Bayesian causal model, sensitive to both confounding and stochasticity, than a purely error-driven algorithm, sensitive only to stochasticity. Implications for the detection and estimation of distinct forms of uncertainty, and for a neural mediation of domain-general constraints on causal induction, are discussed.

scholarly journals Confirmation Based on Analogical Inference: Bayes Meets Jeffrey

2019 ◽  
Vol 50 (2) ◽  
pp. 174-194
Author(s):  
Christian J. Feldbacher-Escamilla ◽  
Alexander Gebharter
Keyword(s):  
Analogical Reasoning ◽  
Moral Reasons ◽  
Analogical Inference ◽  
Jeffrey Conditionalization ◽  
Bayesian Update

AbstractCertain hypotheses cannot be directly confirmed for theoretical, practical, or moral reasons. For some of these hypotheses, however, there might be a workaround: confirmation based on analogical reasoning. In this paper we take up Dardashti, Hartmann, Thébault, and Winsberg’s (2019) idea of analyzing confirmation based on analogical inference Bayesian style. We identify three types of confirmation by analogy and show that Dardashti et al.’s approach can cover two of them. We then highlight possible problems with their model as a general approach to analogical inference and argue that these problems can be avoided by supplementing Bayesian update with Jeffrey conditionalization.

Relational Reasoning in a Neurally Plausible Cognitive Architecture

2005 ◽  
Vol 14 (3) ◽  
pp. 153-157 ◽  
Author(s):  
John E. Hummel ◽  
Keith J. Holyoak
Keyword(s):  
Analogical Reasoning ◽  
A Priori ◽  
Cognitive Architecture ◽  
Relational Reasoning ◽  
Natural Basis ◽  
Analogical Inference ◽  
Analogical Mapping ◽  
Design Requirements ◽  
Human Thinking ◽  
Propositional Structures

Human mental representations are both flexible and structured—properties that, together, present challenging design requirements for a model of human thinking. The Learning and Inference with Schemas and Analogies (LISA) model of analogical reasoning aims to achieve these properties within a neural network. The model represents both relations and objects as patterns of activation distributed over semantic units, integrating these representations into propositional structures using synchrony of firing. The resulting propositional structures serve as a natural basis for memory retrieval, analogical mapping, analogical inference, and schema induction. The model also provides an a priori account of the limitations of human working memory and can simulate the effects of various kinds of brain damage on thinking.

The Diagnosis of Mental Disorders Is Influenced by Automatic Causal Reasoning

2017 ◽  
Vol 6 (2) ◽  
pp. 177-188 ◽  
Author(s):  
Amanda Flores ◽  
Pedro L. Cobos ◽  
York Hagmayer
Keyword(s):  
Experimental Study ◽  
Mental Disorders ◽  
Causal Reasoning ◽  
Causal Model ◽  
Diagnostic Classification ◽  
Causal Knowledge ◽  
Intuitive Reasoning ◽  
Automatic Reasoning ◽  
Causal Coherence ◽  
Study Participants

Causal knowledge has been shown to affect diagnostic decisions. It is unclear, however, how causal knowledge affects diagnosis. We hypothesized that it influences intuitive reasoning processes. More precisely, we speculated that people automatically assess the coherence between observed symptoms and an assumed causal model of a disorder, which in turn affects diagnostic classification. Intuitive causal reasoning was investigated in an experimental study. Participants were asked to read clinical reports before deciding on a diagnosis. Intuitive processing was studied by analyzing reading times. It turned out that reading times were slower when causally expected consequences of present symptoms were missing or effects of absent causes were present. This causal incoherence effect was predictive of participants’ later explicit diagnostic judgments. These and related findings suggest that diagnostic judgments rely on automatic reasoning processes based on the computation of causal coherence. Potential implications of these results for the training of clinicians are discussed.

scholarly journals Neural Correlates of Causal Confounding

2018 ◽  
Author(s):  
Mimi Liljeholm
Keyword(s):  
Prefrontal Cortex ◽  
Computational Modeling ◽  
Causal Model ◽  
Neural Substrates ◽  
Neural Correlates ◽  
Learning Context ◽  
Causal Knowledge ◽  
Dorsomedial Prefrontal Cortex ◽  
General Constraints ◽  
Adults And Children

AbstractAs scientists, we are keenly aware that if putative causes perfectly co-vary, the independent influence of neither can be discerned – a “no confounding” constraint on inference, fundamental to philosophical and statistical perspectives on causation. Intriguingly, a substantial behavioral literature suggests that naïve human reasoners, adults and children, are tacitly sensitive to causal confounding. Here, a combination of fMRI and cognitive computational modeling was used to investigate neural substrates mediating such sensitivity. While being scanned, participants observed and judged the influences of various putative causes with confounded or non-confounded, deterministic or stochastic, influences. During judgments requiring generalization of causal knowledge from a feedback-based learning context to a transfer probe, activity in the dorsomedial prefrontal cortex (DMPFC) was better accounted for by a Bayesian causal model, sensitive to both confounding and stochasticity, than a purely error-driven algorithm, sensitive only to stochasticity. Implications for the detection and estimation of distinct forms of uncertainty, and for a neural mediation of domain general constraints on causal induction, are discussed.

scholarly journals Defining Metabolic Syndrome: Which Kind of Causality, if any, is Required?

Disputatio ◽  
2017 ◽  
Vol 9 (47) ◽  
pp. 553-580
Author(s):  
Margherita Benzi
Keyword(s):  
Metabolic Syndrome ◽  
Causal Model ◽  
Causal Structure ◽  
Causal Knowledge ◽  
Underlying Mechanisms ◽  
Definition Of ◽  
Type 3 ◽  
Causal Specificity

Abstract The definition of metabolic syndrome (MetS) has been, and still is, extremely controversial. My purpose is not to give a solution to the associated debate but to argue that the controversy is at least partially due to the different ‘causal content’ of the various definitions: their theoretical validity and practical utility can be evaluated by reconstructing or making explicit the underlying causal structure. I will therefore propose to distinguish the alternative definitions according to the kinds of causal content they carry: (1) definitions grounded on associations, (2) definitions presupposing a causal model built upon statistical associations, and (3) definitions grounded on underlying mechanisms. I suggest that analysing definitions according to their causal content can be helpful in evaluating alternative definitions of some diseases. I want to show how the controversy over MetS suggests a distinction among three kinds of definitions based on how explicitly they characterise the syndrome in causal terms, and on the type of causality involved. I will call ‘type 1 definitions’ those definitions that are purely associative; ‘type 2 definitions’ the definitions based on statistical associations, plus generic medical and causal knowledge; and ‘type 3 definitions’ the definitions based on (hypotheses about) mechanisms. These kinds of definitions, although different, can be related to each other. A definition with more specific causal content may be useful in the evaluation of definitions characterised by a lower degree of causal specificity. Moreover, the identification of the type of causality involved is of help to constitute a good criterion for choosing among different definitions of a pathological entity. In section (1) I introduce the controversy about MetS, in section (2) I propose some remarks about medical definitions and their ‘causal import’, and in section (3) I suggest that the different attitudes towards the definition of MetS are relevant to evaluate their explicative power.

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