Relevant bibliographies by topics / SAPPhIRE Model of Causality

Academic literature on the topic 'SAPPhIRE Model of Causality'

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Published: 6 September 2023

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Journal articles on the topic "SAPPhIRE Model of Causality"

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Bhatt, Apoorv Naresh, Anubhab Majumder, and Amaresh Chakrabarti. "Analyzing the modes of reasoning in design using the SAPPhIRE model of causality and the Extended Integrated Model of Designing." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 35, no. 4 (November 2021): 384–403. http://dx.doi.org/10.1017/s0890060421000214.

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AbstractLiterature suggests that people typically understand knowledge by induction and produce knowledge by synthesis. This paper revisits the various modes of reasoning – explanatory abduction, innovative abduction, deduction, and induction – that have been proposed by earlier researchers as crucial modes of reasoning underlying the design process. First, our paper expands earlier work on abductive reasoning – an essential mode of reasoning involved in the process of synthesis – by understanding its role with the help of the “SAPPhIRE” model of causality. The explanations of abductive reasoning in design using the SAPPhIRE model have been compared with those using existing models. Second, the paper captures and analyzes various modes of reasoning during design synthesis with the help of the “Extended Integrated Model of Designing”. The analysis of participants' verbal speech and outcomes shows the model's ability to explain the various modes of reasoning that occur in design. The results indicate the above models to provide a more extensive account of reasoning in design synthesis. Earlier empirical validation of both the models lends further support to the claim of their explanatory capacity.
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Sartori, Julian, Ujjwal Pal, and Amaresh Chakrabarti. "A methodology for supporting “transfer” in biomimetic design." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 24, no. 4 (October 25, 2010): 483–506. http://dx.doi.org/10.1017/s0890060410000351.

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AbstractBiomimetics involves transfer from one or more biological examples to a technical system. This study addresses four questions. What are the essential steps in a biomimetic process? What is transferred? How can the transferred knowledge be structured in a way useful for biologists and engineers? Which guidelines can be given to support transfer in biomimetic design processes? In order to identify the essential steps involved in carrying out biomimetics, several procedures found in the literature were summarized, and four essential steps that are common across these procedures were identified. For identification of mechanisms for transfer, 20 biomimetic examples were collected and modeled according to a model of causality called the SAPPhIRE model. These examples were then analyzed for identifying the underlying similarity between each biological and corresponding analogue technical system. Based on the SAPPhIRE model, four levels of abstraction at which transfer takes place were identified. Taking into account similarity, the biomimetic examples were assigned to the appropriate levels of abstraction of transfer. Based on the essential steps and the levels of transfer, guidelines for supporting transfer in biomimetic design were proposed and evaluated using design experiments. The 20 biological and analogue technical systems that were analyzed were similar in the physical effects used and at the most abstract levels of description of their functionality, but they were the least similar at the lowest levels of abstraction: the parts involved. Transfer most often was carried out at the physical effect level of abstraction. Compared to a generic set of guidelines based on the literature, the proposed guidelines improved design performance by about 60%. Further, the SAPPhIRE model turned out to be a useful representation for modeling complex biological systems and their functionality. Databases of biological systems, which are structured using the SAPPhIRE model, have the potential to aid biomimetic concept generation.
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Bhattacharya, Kausik, and Amaresh Chakrabarti. "A KNOWLEDGE GRAPH AND RULE BASED REASONING METHOD FOR EXTRACTING SAPPHIRE INFORMATION FROM TEXT." Proceedings of the Design Society 3 (June 19, 2023): 221–30. http://dx.doi.org/10.1017/pds.2023.23.

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AbstractRepresentation of design information using causal ontologies is very effective for creative ideation in product design. Hence researchers created databases with models of engineering and biological systems using causal ontologies. Manually building many models using technical documents requires significant effort by specialists. Researchers worked on the automatic extraction of design information leveraging the computational techniques of Machine Learning. But these methods are data intensive, have manual touch points and have not yet reported the end-to-end performance of the process. In this paper, we present the results of a new method inspired by the cognitive process followed by specialists. This method uses the Knowledge Graph with Rule based reasoning for information extraction for the SAPPhIRE causality model from natural language texts. Unlike the supervised learning methods, this new method does not require data intensive modelling. We report the performance of the end-to-end information extraction process, which is found to be a promising alternative.
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Srinivasan, V., and Amaresh Chakrabarti. "Investigating novelty–outcome relationships in engineering design." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 24, no. 2 (April 26, 2010): 161–78. http://dx.doi.org/10.1017/s089006041000003x.

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AbstractDesign creativity involves developing novel and useful solutions to design problems. The research in this article is an attempt to understand how novelty of a design resulting from a design process is related to the kind of outcomes, described here as constructs, involved in the design process. A model of causality, the SAPPhIRE model, is used as the basis of the analysis. The analysis is based on previous research that shows that designing involves development and exploration of the seven basic constructs of the SAPPhIRE model that constitute the causal connection between the various levels of abstraction at which a design can be described. The constructs are state change, action, parts, phenomenon, input, organs, and effect. The following two questions are asked. Is there a relationship between novelty and the constructs? If there is a relationship, what is the degree of this relationship? A hypothesis is developed to answer the questions: an increase in the number and variety of ideas explored while designing should enhance the variety of concept space, leading to an increase in the novelty of the concept space. Eight existing observational studies of designing sessions are used to empirically validate the hypothesis. Each designing session involves an individual designer, experienced or novice, solving a design problem by producing concepts and following a think-aloud protocol. The results indicate dependence of novelty of concept space on variety of concept space and dependence of variety of concept space on variety of idea space, thereby validating the hypothesis. The results also reveal a strong correlation between novelty and the constructs; correlation value decreases as the abstraction level of the constructs reduces, signifying the importance of using constructs at higher abstraction levels for enhancing novelty.
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Chakrabarti, Amaresh, V. Srinivasan, B. S. C. Ranjan, and Udo Lindemann. "A case for multiple views of function in design based on a common definition." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 27, no. 3 (July 24, 2013): 271–79. http://dx.doi.org/10.1017/s0890060413000279.

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AbstractFunctions are important in designing. However, several issues hinder progress with the understanding and usage of functions: lack of a clear and overarching definition of function, lack of overall justifications for the inevitability of the multiple views of function, and scarcity of systematic attempts to relate these views with one another. To help resolve these, the objectives of this research are to propose a common definition of function that underlies the multiple views in literature and to identify and validate the views of function that are logically justified to be present in designing. Function is defined as a change intended by designers between two scenarios: before and after the introduction of the design. A framework is proposed that comprises the above definition of function and an empirically validated model of designing, extended generate, evaluate, modify, and select of state-change, and an action, part, phenomenon, input, organ, and effect model of causality (Known as GEMS of SAPPhIRE), comprising the views of activity, outcome, requirement–solution–information, and system–environment. The framework is used to identify the logically possible views of function in the context of designing and is validated by comparing these with the views of function in the literature. Describing the different views of function using the proposed framework should enable comparisons and determine relationships among the various views, leading to better understanding and usage of functions in designing.
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Finkbeiner, Bernd, and Andrey Kupriyanov. "Causality-based Model Checking." Electronic Proceedings in Theoretical Computer Science 259 (October 10, 2017): 31–38. http://dx.doi.org/10.4204/eptcs.259.3.

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Iwasaki, Yumi, and Herbert A. Simon. "Causality and model abstraction." Artificial Intelligence 67, no. 1 (May 1994): 143–94. http://dx.doi.org/10.1016/0004-3702(94)90014-0.

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Gibbons, G. W. "Causality and the Skyrme model." Physics Letters B 566, no. 1-2 (July 2003): 171–74. http://dx.doi.org/10.1016/s0370-2693(03)00384-8.

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Lee, Hyun Seop, and Taek Yung Lee. "Material Removal Model of Lap Grinding for Sapphire Substrate Based on Roughness Parameters." Materials Science Forum 890 (March 2017): 384–87. http://dx.doi.org/10.4028/www.scientific.net/msf.890.384.

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Sapphire is one of difficult-to-machine materials because of its high hardness and brittleness. It can be used for an optical window or cover named as sapphire glass and a substrate for semiconductor circuits. Before preparing the required surface roughness of sapphire substrate, the geometrical shape should be retained through mechanical machining processes. The lapping and diamond mechanical polishing (DMP) are essentially used for achieving the required thickness and surface roughness of sapphire substrate prior to chemical mechanical polishing (CMP). In this study, we introduce a lap grinding process using fixed abrasives to substitute lapping and DMP. The material removal rates (MRRs) were measured under various machining conditions. The semi-empirical model on MRR was introduced based on the information of grinding pallet. This paper may provide a preliminary experimental study on the lap grinding of sapphire substrate.
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Martinussen, Torben. "Causality and the Cox Regression Model." Annual Review of Statistics and Its Application 9, no. 1 (March 7, 2022): 249–59. http://dx.doi.org/10.1146/annurev-statistics-040320-114441.

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This article surveys results concerning the interpretation of the Cox hazard ratio in connection to causality in a randomized study with a time-to-event response. The Cox model is assumed to be correctly specified, and we investigate whether the typical end product of such an analysis, the estimated hazard ratio, has a causal interpretation as a hazard ratio. It has been pointed out that this is not possible due to selection. We provide more insight into the interpretation of hazard ratios and differences, investigating what can be learned about a treatment effect from the hazard ratio approaching unity after a certain period of time. The conclusion is that the Cox hazard ratio is not causally interpretable as a hazard ratio unless there is no treatment effect or an untestable and unrealistic assumption holds. We give a hazard ratio that has a causal interpretation and study its relationship to the Cox hazard ratio.
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Dissertations / Theses on the topic "SAPPhIRE Model of Causality"

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Johansson, Magnus, and Johan Kingstedt. "Methods for Residual Generation Using Mixed Causality in Model Based Diagnosis." Thesis, Linköping University, Department of Electrical Engineering, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-12062.

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Several different air pollutions are produced during combustion in a diesel engine, for example nitric oxides, NOx, which can be harmful for humans. This has led to stricter emission legislations for heavy duty trucks. The law requires both lower emissions and an On-Board Diagnosis system for all manufactured heavy duty trucks. The OBD system supervises the engine in order to keep the emissions below legislation demands. The OBD system shall detect malfunctions which may lead to increased emissions. To design the OBD system an automatic model based diagnosis approach has been developed at Scania CV AB where residual generators are generated from an engine model.

The main objective of this thesis is to improve the existing methods at Scania CV AB to extract residual generators from a model in order to generate more residual generators. The focus lies on the methods to find possible residual generators given an overdetermined subsystem. This includes methods to estimate derivatives of noisy signals.

A method to use both integral and derivative causality has been developed, called mixed causality. With this method it has been shown that more residual generators can be found when designing a model based diagnosis system, which improves the fault isolation. To use mixed causality, derivatives are estimated with smoothing spline approximation.

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Guo, Yuanxiang. "Chinese wheat price analysis - with application of cointegration and Granger causality test." Thesis, Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/52978.

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Traditional demonstration of price fluctuation in the wheat market, by the theory of supply and demand is not comprehensive enough. With limited understanding of macroeconomic effects on the wheat market, accurate prediction of wheat price is impossible. Given the Chinese self—sustainable food policy, grain imports is a sensitive topic which may incur fierce argument. In this paper, however, I emphasize effect of exchange rate on nominal wheat price. By application of the cointegration theory, CPI shows slight negative correlation with nominal wheat price, yet GDP and population move in the same direction as the wheat price. The cointegration study of exchange rate implies, with appreciating Chinese RMB, domestic buyers incline to purchase wheat from the cheaper foreign market. According to the Granger causality test, the whole package of variables suggests significant causal relation with the wheat price.
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Vang, Jee. "Using a model of human cognition of causality to orient arcs in structural learning of Bayesian networks." Fairfax, VA : George Mason University, 2008. http://hdl.handle.net/1920/3386.

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Thesis (Ph.D.)--George Mason University, 2008.
Vita: p. 249. Thesis director: Farrokh Alemi. Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computational Sciences and Informatics. Title from PDF t.p. (viewed Mar. 16, 2009). Includes bibliographical references (p. 238-248). Also issued in print.
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Hubálek, Ondřej. "Grafické modely ve statistice a ekonometrii." Master's thesis, Vysoká škola ekonomická v Praze, 2012. http://www.nusl.cz/ntk/nusl-165317.

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Graphical models in statistics and econometrics provide capability to describe causal relations using causal graph in classical regression analysis and others econometric tools. Goal of this thesis is description of causal modelling of time series with help of structural models of vector autoregression. There is description of procedure of building structural VAR model, principle of graphical models and building model for causal dependence analysis. For purpose of comparison there are used data from both USA and Czech Republic and comparison of similar models for both countries is presented. Best models are then selected, to show causal relations between macroeconomic variables. For purpose of analysis, impulse-response functions are used to show impact of demand shock on GDP and other macro indicators.
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Omran, Hayan. "Examining the relationship between trading volume, market return volatility and U.S. aggregate mutual fund flow." Thesis, Brunel University, 2016. http://bura.brunel.ac.uk/handle/2438/12848.

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This thesis consists of three studies which cover topics in the trading volume-market return volatility linkage, stock market return-aggregate mutual fund flow relationship as well as market return volatility-aggregate mutual fund flow interaction. Chapter 2 investigates the issue of volume-volatility linkage in the US market for the period 1990-2012 (S&P 500) and 1992-2012 (Dow Jones). We construct four sub-samples depending on three different structural points (the Asian Financial Crisis, the Dot-Com Bubble and the 2007 Financial Crisis). By employing univariate and bivariate GARCH processes, we find positive (negative) bidirectional linkages between these two aforementioned variables in various cases of the estimation, while a mixed one is observed in the remainder of these cases. Chapter 3 examines the issue of temporal ordering of the range-based stock market return (S&P 500 index) and aggregate mutual fund flow in the U.S. market for the period 1998-2012. We construct nine sub-samples represented by three fundamental cases of the whole data set. In addition, we take into consideration three essential indicators when splitting the whole data set, which are the 2000 Dot-Com Bubble, the 2007 Financial Crisis as well as the 2009 European Sovereign Debt Crisis. We examine the dynamics of the return-flow interaction by employing bivariate VAR model with various specifications of GARCH approach. Our principal findings display a bidirectional mixed feedback between stock market return and aggregate mutual fund flow for the majority of the sub-samples obtained. Nevertheless, we provide limited evidence of a positive bi-directional causality between return and flow. Chapter 4 investigates the dynamic relation between S&P 500 return volatility and U.S. aggregate mutual fund flow for the period spanning between 1998 and 2012. We assess the dynamics of the volatility-flow linkage by employing a bivariate VAR model with the GARCH approach which allows for long memory in the mean and the variance equations. In addition to the sub-samples obtained in chapter 3, we generate two measurements of volatility. Our baseline results indicate a variety of bidirectional mixed causalities between market return volatility and aggregate mutual fund flow in several sub-samples. In addition, we observe a negative/positive bi-directional relationship between volatility and flow in the rest of the sub-periods. Summarizing, a range of our findings are in line with the empirical underpinnings that most likely predict a significant linkage between the aforementioned variables. Finally, most of the bidirectional effects are found to be quite robust to the dynamics of the various GARCH processes employed in this thesis.
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Kayesh, Humayun. "Deep Learning for Causal Discovery in Texts." Thesis, Griffith University, 2022. http://hdl.handle.net/10072/415822.

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Causality detection in text data is a challenging natural language processing task. This is a trivial task for human beings as they acquire vast background knowledge throughout their lifetime. For example, a human knows from their experience that heavy rain may cause flood or plane accidents may cause death. However, it is challenging to automatically detect such causal relationships in texts due to the availability of limited contextual information and the unstructured nature of texts. The task is even more challenging for social media short texts such as Tweets as often they are informal, short, and grammatically incorrect. Generating hand-crafted linguistic rules is an option but is not always effective to detect causal relationships in text because they are rigid and require grammatically correct sentences. Also, the rules are often domain-specific and not always portable to another domain. Therefore, supervised learning techniques are more appropriate in the above scenario. Traditional machine learning-based model also suffers from the high dimensional features of texts. This is why deep learning-based approaches are becoming increasingly popular for natural language processing tasks such as causality detection. However, deep learning models often require large datasets with high-quality features to perform well. Extracting deeply-learnable causal features and applying them to a carefully designed deep learning model is important. Also, preparing a large human-labeled training dataset is expensive and time-consuming. Even if a large training dataset is available, it is computationally expensive to train a deep learning model due to the complex structure of neural networks. We focus on addressing the following challenges: (i) extracting highquality causal features, (ii) designing an effective deep learning model to learn from the causal features, and (iii) reducing the dependency on large training datasets. Our main goals in this thesis are as follows: (i) we aim to study the different aspects of causality and causal discovery in text in depth. (ii) We aim to develop strategies to model causality in text, (iii) and finally, we aim to develop frameworks to design effective and efficient deep neural network structures to discover causality in texts.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Info & Comm Tech
Science, Environment, Engineering and Technology
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Karl, Velander, and Callerud Karin. "The development of the financialsystem and economic growth in Sweden : A Granger causality analysis." Thesis, Karlstads universitet, Handelshögskolan (from 2013), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-78703.

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Binase, Uviwe. "Socioeconomic determinants of life expectancy in post-apartheid South Africa." University of the Western Cape, 2018. http://hdl.handle.net/11394/6790.

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Magister Philosophiae - MPhil
Life expectancy in South African has been fluctuating following the global trends that affects both developed and developing countries. In South Africa the average life expectancy from 1994 to 1996 was higher with an average of 61,3 years. As from 1997 to 1999 it declined to an average of 58,4 years. The difference in years between 1994-1996 and 1997- 1999 was 2,9 years. From 2000-2002, life expectancy continued to decline to an average of 54,6 years. Life expectancy declined in a constant proportion from 2003-2005 and 2006-2008. In 2003-2005 it slightly declined to 52 years and in 2004-2007 it declined to 42,0 years. Life expectancy escalated after the mentioned years to 54,4 years between 2009-2011 and from 2012-2013 life expectancy was 54,0 years on average. This study examined factors or variables that verify the socioeconomic determinants of life expectancy in post-apartheid South Africa. Understanding the relationship between life expectancy and the socioeconomic variables was based on three objectives. The main objective for this study was to determine the impact of socioeconomic variables and health policy efforts on life expectancy, seeking an in-depth understanding by investigating the causality relationship between life expectancy and socioeconomic variables thus later investigating the difference between male and female’s life expectancy. This study was motivated by the fluctuating life expectancy in South Africa. The fluctuation in life expectancy were thus studied in relation to socioeconomic determinants which are government health expenditure, government education expenditure, GDP per capita, total fertility rate, urban population, access to sustainable drinking water and undernourishment. The mentioned variables were used as socioeconomic determinants of life expectancy during post-apartheid South Africa.
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Strikholm, Birgit. "Essays on nonlinear time series modelling och hypothesis testing." Doctoral thesis, Handelshögskolan i Stockholm, Ekonomisk Statistik (ES), 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:hhs:diva-535.

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There seems to be a common understanding nowadays that the economy is nonlinear. Economic theory suggests features that can not be incorporated into linear frameworks, and over the decades a solid body of empirical evidence of nonlinearities in economic time series has been gathered. This thesis consists of four essays that have to do with various forms of nonlinear statistical inference. In the first chapter the problem of determining the number regimes in a threshold autoregressive (TAR) model is considered. Typically, the number of regimes (or thresholds) is assumed unknown and has to be determined from the data. The solution provided in the chapter first uses the smooth transition autoregressive (STAR) model with a fixed and rapid transition to approximate the TAR model. The number of thresholds is then determined using sequential misspecification tests developed for the STAR model.  The main characteristic of the proposed method is that only standard statistical inference is used, as opposed to non-standard inference or computation intensive bootstrap-based methods. In the second chapter a similar idea is employed and the structural break model is approximated with a smoothly time-varying autoregressive model. By making the smooth changes in parameters rapid, the model is able to closely approximate the corresponding model with breaks in the parameter structure. This approximation makes the misspecification tests developed for the STR modelling framework available and they can be used for sequentially determining the number of breaks. Again, the method is computationally simple as all tests rely on standard statistical inference. There exists literature suggesting that business cycle fluctuations affect the pattern of seasonality in macroeconomic series. A question asked in the third chapter is whether other factors such as changes in institutions or technological change may have this effect as well. The time-varying smooth transition autoregressive (TV- STAR) models that can incorporate both types of change are used to model the (possible) changes in seasonal patterns and shed light on the hypothesis that institutional and technological changes (proxied by time) may have a stronger effect on seasonal patterns than business cycle. The TV-STAR testing framework is applied to nine quarterly industrial production series from the G7 countries, Finland and Sweden. These series display strong seasonal patterns and also contain the business cycle fluctuations. The empirical results of the chapter suggest that seasonal patterns in these series have been changing over time and, furthermore, that the business cycle fluctuations do not seem to be the main cause for this change. The last chapter of the thesis considers the possibility of testing for Granger causality in bivariate nonlinear systems when the exact form of the nonlinear relationship between variables is not known. The idea is to linearize the testing problem by approximating the nonlinear system by its Taylor expansion. The expansion is linear in parameters and one gets round the difficulty caused by the unknown functional form of the relationship under investigation.

Diss. Stockholm : Handelshögskolan, 2004

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Germeys, Jasper. "Supervision of the Air Loop in the Columbus Module of the International Space Station." Thesis, Linköpings universitet, Fordonssystem, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-133926.

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Failure detection and isolation (FDI) is essential for reliable operations of complex autonomous systems or other systems where continuous observation or maintenance thereof is either very costly or for any other reason not easily accessible. Beneficial for the model based FDI is that there is no need for fault data to detect and isolate a fault in contrary to design by data clustering. However, it is limited by the accuracy and complexity of the model used. As models grow more complex, or have multiple interconnections, problems with the traditional methods for FDI emerge. The main objective of this thesis is to utilise the automated methodology presented in [Svärd, 2012] to create a model based FDI system for the Columbus air loop. A small but crucial part of the life support on board the European space laboratory Columbus. The process of creating a model based FDI, from creation of the model equations, validation thereof to the design of residuals, test quantities and evaluation logic is handled in this work. Although the latter parts only briefly which leaves room for future work. This work indicate that the methodology presented is capable to create quite decent model based FDI systems even with poor sensor placement and limited information of the actual design. [] Carl Svärd. Methods for Automated Design of Fault Detection and Isolation Systems with Automotive Applications. PhD thesis, Linköping University, Vehicular Systems, The Institute of Technology, 2012
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Books on the topic "SAPPhIRE Model of Causality"

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Long, Derek. Reasoning by analogy and causality: A model and application. New York: Ellis Horwood, 1994.

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Huffaker, Ray, Marco Bittelli, and Rodolfo Rosa. Empirically Detecting Causality. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198782933.003.0008.

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Phenomenological models mathematically describe relationships among empirically observed phenomena without attempting to explain underlying mechanisms. Within the context of NLTS, phenomenological modeling goes beyond phase space reconstruction to extract equations governing real-world system dynamics from a single or multiple observed time series. Phenomenological models provide several benefits. They can be used to characterize the dynamics of variable interactions; for example, whether an incremental increase in one variable drives a marginal increase/decrease in the growth rate of another, and whether these dynamic interactions follow systematic patterns over time. They provide an analytical framework for data driven science still searching for credible theoretical explanation. They set a descriptive standard for how the real world operates so that theory is not misdirected in explaining fanciful behavior. The success of phenomenological modeling depends critically on selection of governing parameters. Model dimensionality, and the time delays used to synthesize dynamic variables, are guided by statistical tests run for phase space reconstruction. Other regression and numerical integration parameters can be set on a trial and error basis within ranges providing numerical stability and successful reproduction of empirically-detected dynamics. We illustrate phenomenological modeling with solutions of the Lorenz model so that we can recognize the dynamics that need to be reproduced.
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Hagmayer, York, and Philip Fernbach. Causality in Decision-Making. Edited by Michael R. Waldmann. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199399550.013.27.

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Although causality is rarely discussed in texts on decision-making, decisions often depend on causal knowledge and causal reasoning. This chapter reviews what is known about how people integrate causal considerations into their choice processes. It first introduces causal decision theory, a normative theory of choice based on the idea that rational decision-making requires considering the causal structure underlying a decision problem. It then provides an overview of empirical studies that explore how causal assumptions influence choice and test predictions derived from causal decision theory. Next it reviews three descriptive theories that integrate causal thinking into decision-making, each in a different way: the causal model theory of choice, the story model of decision-making, and attribution theory. It discusses commonalities and differences between the theories and the role of causality in other decision-making theories. It concludes by noting challenges that lie ahead for research on the role of causal reasoning in decision-making.
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Malmgren, Helge. The theoretical basis of the biopsychosocial model. Oxford University Press, 2015. http://dx.doi.org/10.1093/med:psych/9780198530343.003.0002.

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This chapter addresses the philosophy behind the biopsychosocial model. It summarizes five aetiological problems that the biopsychosocial model must address (nature versus nurture; single-factor versus multifactor causality; somatic versus mental causes; reasons versus causes; conscious versus non-conscious influences) with a particular focus on the mind-body problem, and uses an analogy between computer hardware and software to describe the relationship between the mind and body.
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Solstad, Torgrim, and Oliver Bott. Causality and Causal Reasoning in Natural Language. Edited by Michael R. Waldmann. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199399550.013.32.

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This chapter provides a combined overview of theoretical and psycholinguistic approaches to causality in language. The chapter’s main phenomenological focus is on causal relations as expressed intra-clausally by verbs (e.g., break, open) and between sentences by discourse markers (e.g., because, therefore). Special attention is given to implicit causality verbs that are argued to trigger expectations of explanations to occur in subsequent discourse. The chapter also discusses linguistic expressions that do not encode causation as such, but that seem to be dependent on a causal model for their adequate evaluation, such as counterfactual conditionals. The discussion of the phenomena is complemented by an overview of important aspects of their cognitive processing as revealed by psycholinguistic experimentation.
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Brady, Henry E. Causation and Explanation in Social Science. Edited by Janet M. Box-Steffensmeier, Henry E. Brady, and David Collier. Oxford University Press, 2009. http://dx.doi.org/10.1093/oxfordhb/9780199286546.003.0010.

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This article provides an overview of causal thinking by characterizing four approaches to causal inference. It also describes the INUS model. It specifically presents a user-friendly synopsis of philosophical and statistical musings about causation. The four approaches to causality include neo-Humean regularity, counterfactual, manipulation and mechanisms, and capacities. A counterfactual is a statement, typically in the subjunctive mood, in which a false or ‘counter to fact’ premise is followed by some assertion about what would have happened if the premise were true. Three basic questions about causality are then addressed. Moreover, the article gives a review of four approaches of what causality might be. It pays attention on a counterfactual definition, mostly amounting to a recipe that is now widely used in statistics. It ends with a discussion of the limitations of the recipe and how far it goes toward solving the epistemological and ontological problems.
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McCleary, Richard, David McDowall, and Bradley J. Bartos. Internal Validity. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190661557.003.0007.

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Chapter 7 begins with an outline and description of five threats to internal validity common to time series designs: history, maturation, instrumentation, regression, and selection. Given the fundamental role of prediction in the modern scientific method, scientific hypotheses are necessarily causal. After an outline of the evolving definition of “causality” in the social sciences, contemporary Rubin causality or counterfactual causality is introduced. Under the assumption that subjects were randomly assigned to the treatment and control groups, Rubin’s causal model allows one to estimate the unobserved causal parameter from observed data. Control time series are chosen so as to render plausible threats to internal validity implausible. An appropriate control time series may not exist, however, an ideal time series may be possible to construct. Synthetic control group models construct a control time series that optimally recreates the treated unit’s preintervention trend using a combination of untreated donor pool units.
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Boland, Lawrence A. Equilibrium concepts and critiques. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190274320.003.0006.

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This chapter explores the equilibrium concept by examining the views of two cultures: those who began talking about equilibrium models in the decades before World War II and those formal model builders promoting mathematics after that war. For the older culture, the concept of an equilibrium refers to the real properties of an actual economy in a state of equilibrium. For the newer culture, an equilibrium refers only to a property of a formal mathematical model. The main discussion of the chapter is about the various critiques provided by both sides of the cultural divide. The chapter also discusses the extent to which the distinction between a model’s exogenous vs. endogenous variables involves causality. The older culture would view causality as a necessary part of understanding an equilibrium but the newer culture would view it only as an interpretation of the mathematics of the model.
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Wendling, Fabrice, Marco Congendo, and Fernando H. Lopes da Silva. EEG Analysis. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0044.

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This chapter addresses the analysis and quantification of electroencephalographic (EEG) and magnetoencephalographic (MEG) signals. Topics include characteristics of these signals and practical issues such as sampling, filtering, and artifact rejection. Basic concepts of analysis in time and frequency domains are presented, with attention to non-stationary signals focusing on time-frequency signal decomposition, analytic signal and Hilbert transform, wavelet transform, matching pursuit, blind source separation and independent component analysis, canonical correlation analysis, and empirical model decomposition. The behavior of these methods in denoising EEG signals is illustrated. Concepts of functional and effective connectivity are developed with emphasis on methods to estimate causality and phase and time delays using linear and nonlinear methods. Attention is given to Granger causality and methods inspired by this concept. A concrete example is provided to show how information processing methods can be combined in the detection and classification of transient events in EEG/MEG signals.
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Barham, Jeremy. Mahler and the Game of History. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780199316090.003.0017.

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For obvious reasons, the understanding and writing of music history have favoured a linear model founded in causality and chronology. Like many disciplines, however, historiographical studies have been subjected to critiques of various theoretical and imaginative types, particularly, but not exclusively, in recent times. These critiques are outlined here, and three historiographical models critically applied to the understanding of Mahler’s music: historicism, historical materialism (after Walter Benjamin), and a more radical rhizomatic model (after Deleuze). Posited, put into operation and questioned, these models cast multi-perspectival and multi-temporal light on how Mahler’s music continues to participate in contexts of contemporary mass-media and public consciousness.
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Book chapters on the topic "SAPPhIRE Model of Causality"

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Bhattacharya, Kausik, Apoorv Naresh Bhatt, B. S. C. Ranjan, Sonal Keshwani, V. Srinivasan, and Amaresh Chakrabarti. "Extracting Information for Creating SAPPhIRE Model of Causality from Natural Language Descriptions." In Design Computing and Cognition’22, 3–20. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-20418-0_1.

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Bhattacharya, Kausik, and Amaresh Chakrabarti. "Application of SAPPhIRE Model of Causality in the Design of Product-Service Systems." In Design in the Era of Industry 4.0, Volume 3, 527–39. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0428-0_43.

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Young, Gerald. "Postformal Thought: Commons’ Model." In Development and Causality, 413–37. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9422-6_18.

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Leitner-Fischer, Florian, and Stefan Leue. "On the Synergy of Probabilistic Causality Computation and Causality Checking." In Model Checking Software, 246–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39176-7_16.

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Young, Gerald. "Generic Change Model." In Unifying Causality and Psychology, 807–31. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-24094-7_33.

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Young, Gerald. "Introducing the 25-Step Neo-Eriksonian Model." In Causality and Development, 271–81. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-02493-2_12.

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Young, Gerald. "The Present Cognitive-Affective Stage Model: I Cognition." In Development and Causality, 73–89. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9422-6_4.

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Wolf, Karsten. "Model Checking Concurrency and Causality." In Application and Theory of Petri Nets and Concurrency, 159–78. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57861-3_11.

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M. Steiner, Peter. "Design- and Model-Based Analysis of Propensity Score Designs." In Statistics and Causality, 333–61. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781118947074.ch14.

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Young, Gerald. "The Present Model: II Postformal Stage, Neo-Eriksonian Parallels, Systems." In Development and Causality, 91–112. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9422-6_5.

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Conference papers on the topic "SAPPhIRE Model of Causality"

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Guo Li, Jianmin Gao, and Fumin Chen. "Construction of causality diagram model for diagnostics." In 2008 Annual Reliability and Maintainability Symposium. IEEE, 2008. http://dx.doi.org/10.1109/rams.2008.4925777.

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Yu, Jiezhou, Jun Meng, and Changkai Sun. "An intelligent model with chaos and causality." In 2020 Chinese Control And Decision Conference (CCDC). IEEE, 2020. http://dx.doi.org/10.1109/ccdc49329.2020.9163992.

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I. Hammad, Hussain, and D. J. Verschuur. "Causality-constrained model-independent joint migration inversion." In SEG Technical Program Expanded Abstracts 2018. Society of Exploration Geophysicists, 2018. http://dx.doi.org/10.1190/segam2018-2998276.1.

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Schneider, Klaus, and Jens Brandt. "Performing causality analysis by bounded model checking." In 2008 8th International Conference on Application of Concurrency to System Design. IEEE, 2008. http://dx.doi.org/10.1109/acsd.2008.4574599.

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Wu, Chenwang, Xiting Wang, Defu Lian, Xing Xie, and Enhong Chen. "A Causality Inspired Framework for Model Interpretation." In KDD '23: The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3580305.3599240.

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Nazarov, D. M. "Fuzzy model for assessment of causality of factors." In 2017 XX IEEE International Conference on Soft Computing and Measurements (SCM). IEEE, 2017. http://dx.doi.org/10.1109/scm.2017.7970746.

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Liang, Li, Bin Deng, Jiang Wang, Ruofan Wang, Xile Wei, Haitao Yu, Yingmei Qin, and Chen Yang. "Granger causality analysis in the neural mass model." In 2015 34th Chinese Control Conference (CCC). IEEE, 2015. http://dx.doi.org/10.1109/chicc.2015.7260356.

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"Causality, Responsibility, and Blame: A Structural-Model Approach." In Third International Conference on the Quantitative Evaluation of Systems - (QEST'06). IEEE, 2006. http://dx.doi.org/10.1109/qest.2006.9.

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Doyle, Matt, Rohan Mandrekar, and Jason Morsey. "Techniques and considerations for verification of model causality." In 2011 IEEE 61st Electronic Components and Technology Conference (ECTC). IEEE, 2011. http://dx.doi.org/10.1109/ectc.2011.5898556.

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Mao, Wenji, and Jonathan Gratch. "Evaluating a computational model of social causality and responsibility." In the fifth international joint conference. New York, New York, USA: ACM Press, 2006. http://dx.doi.org/10.1145/1160633.1160809.

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Reports on the topic "SAPPhIRE Model of Causality"

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Mao, Wenji, and Jonathan Gratch. Evaluating a Computational Model of Social Causality and Responsibility. Fort Belvoir, VA: Defense Technical Information Center, January 2006. http://dx.doi.org/10.21236/ada459151.

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Manzano, Osmel, and José Luis Saboin. Reverse Causality between Oil Policy and Fiscal Policy?: The Venezuelan Experience. Inter-American Development Bank, May 2021. http://dx.doi.org/10.18235/0003290.

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This paper uses a model of intergenerational accounting to simulate the intergenerational distribution of oil wealth in Venezuela. Venezuelan oil production does not seem to follow an optimal extraction path. Nevertheless, this is true if we do not consider what the government does with the resources received from the oil sector. In this paper we explored the interaction of oil policy and fiscal policy using an intergeneration accounting model. We found that these interactions could explain certain outcomes. In particular, the model could explain why the sector was open for investment in 1991 and then “re-nationalized” in 2001. Results suggest that when fiscal policy could leave an important burden to future generations, voters seem to favor a more tax oriented oil policy, leaving the oil in the subsoil.
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Hajdini, Ina, Edward S. Knotek, John Leer, Mathieu O. Pedemonte, Robert W. Rich, and Raphael S. Schoenle. Low passthrough from inflation expectations to income growth expectations: why people dislike inflation. Federal Reserve Bank of Cleveland, March 2023. http://dx.doi.org/10.26509/frbc-wp-202221r.

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We implement a novel methodology to disentangle two-way causality in inflation and income expectations in a large, nationally representative survey of US consumers. We find a 20 percent passthrough from expected inflation to expected income growth, but no statistically significant effect in the other direction. Passthrough is higher for higher-income individuals and men. Higher inflation expectations increase consumers’ likelihood to search for higherpaying new jobs. In a calibrated search-and-matching model, dampened responses of wages to demand and supply shocks translate into greater output fluctuations. The survey results and model analysis provide a labor market channel for why people dislike inflation.
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Ahwireng-Obeng, Asabea Shirley, and Frederick Ahwireng-Obeng. Private Philanthropic Cross-Border Flows and Sustainable Development in Africa. Centre on African Philanthropy and Social Investment, August 2011. http://dx.doi.org/10.47019/2021.ra1.

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The paper examines the simultaneous impact of private philanthropic cross-border funding from international foundations on the economic, social, and environmental dimensions of sustainable development in Africa. The vector error correction model (VECM) was used, and contrary to expectations drawn from past studies, funding from this source improves economic growth, advances human development, and enhances environmental quality. Causality test results also disconfirmed the assumption that interactions among the three dimensions were positive and complementary in the long term. The environment variable was found to be noncomplementary. Based on these unique results, theoretical propositions are made with an underlying mechanism of action. Practical and policy implications, limitations, and directions for future research are discussed.
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Kneale, Dylan, James Thomas, Mukdarut Bangpan, Hugh Waddington, and David Gough. Causal chain analysis in systematic reviews of international development interventions. Centre for Excellence and Development Impact and Learning (CEDIL), 2018. http://dx.doi.org/10.51744/cip4.

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Understanding the extent to which an intervention ‘works’ can provide compelling evidence to decision-makers, although without an accompanying explanation of how an intervention works, this evidence can be difficult to apply in other settings, ultimately impeding its usefulness in making judicious and evidence-informed decisions. In this paper, we describe causal chain analysis as involving the development of a logic model, which outlines graphically a hypothesis of how an intervention leads to a change in an outcome. This logic model is then used to anchor subsequent decisions in the systematic review process, including decisions on synthesis. In this paper, we outline the steps taken in building a logic model, which usually consists of a series of boxes depicting intervention components and processes, outputs, and outcomes with arrows depicting connecting relationships. The nature of these connecting relationships and their basis in causality are considered, through a focus on complex causal relationships and the way in which contextual factors about the intervention setting or population may moderate these. We also explore the way in which specific combinations of intervention components may lead to successful interventions. Evidence synthesis techniques are discussed in the context of causal chain analysis, and their usefulness in exploring different parts of the causal chain or different types of relationship. The approaches outlined in this paper aim to assist systematic reviewers in producing findings that are useful to decision-makers and practitioners, and in turn, help to confirm existing theories or develop entirely new ways of understanding how interventions to effect change.
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