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causality
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Causal structure of the EFQM excellence model among healthcare sector: a case study in Iran
, Article Total Quality Management and Business Excellence ; 2015 ; 14783363 (ISSN) ; Kamali Miab, A ; Sadeghi, M. J ; Sharif University of Technology
Routledge
2015
Abstract
This paper aims to find the causal structure among the criteria of European Foundation for Quality Management (EFQM) excellence model in the organisations of the healthcare sector to prioritise improvement actions for excellence in hospitals. In this regard, a framework of relations among the criteria of EFQM model is developed by theoretical studies and then, this framework is tested based on the results of self-evaluations performed in Iran public hospitals using the Structural Equation Modelling statistical technique. The required data have been collected from 40 public hospitals from 30 provinces and more than 1200 senior and middle managers of clinical departments of the Iran healthcare...
Causal structure of the EFQM excellence model among healthcare sector: a case study in Iran
, Article Total Quality Management and Business Excellence ; Volume 28, Issue 5-6 , 2017 , Pages 663-677 ; 14783363 (ISSN) ; Kamali Miab, A ; Sadeghi, M. J ; Sharif University of Technology
Routledge
2017
Abstract
This paper aims to find the causal structure among the criteria of European Foundation for Quality Management (EFQM) excellence model in the organisations of the healthcare sector to prioritise improvement actions for excellence in hospitals. In this regard, a framework of relations among the criteria of EFQM model is developed by theoretical studies and then, this framework is tested based on the results of self-evaluations performed in Iran public hospitals using the Structural Equation Modelling statistical technique. The required data have been collected from 40 public hospitals from 30 provinces and more than 1200 senior and middle managers of clinical departments of the Iran healthcare...
Probability and Causation: Based on Epistemological Approach
, M.Sc. Thesis Sharif University of Technology ; Azadegan, Ebrahim (Supervisor)
Abstract
After Hume’s skeptical argument on the concept of causality and establishing the regularity theory of causation by him, it seemed that the regularity theory might not respond to irrelevance problems and spurious regularities. In the second half of the twentieth century, attempts were made, e.g. Hans Reichenbach's proposal of the “common cause principle,” for solving the problems of regularity theory which resulted in probabilistic approach to causal relationships. The importance of the common cause principle is that it has inspired the “Causal Markov Condition” which is the most fundamental condition for causal networks. The causal Markov condition states that a variable X is...
Causal Connectivity Effects between Cardiovascular Signals
, M.Sc. Thesis Sharif University of Technology ; Shamsollahi, Mohammad Bagher (Supervisor)
Abstract
Cardiovascular signals are used to check the performance of the cardiovascular system. Researchers have always been interested in processing cardiovascular signals to achieve goals like diagnosing arrhythmias and heart diseases. The presence of causal or non-causal relationships between these signals can be used to evaluate the cardiovascular system's performance. Cardiovascular signals, such as ECG signal, RR series and systolic arterial blood pressure, chest volume or respiratory force, and blood oxygen concentration signals are used to estimate cardiovascular connectivities. There are different approaches to assessing causal relationships; among these approaches, methods based on Granger...
Should methodological naturalists commit to metaphysical naturalism?
, Article Journal for General Philosophy of Science ; Volume 51, Issue 1 , 2020 , Pages 185-193 ; Azadegan, E ; Nabavi, L ; Sharif University of Technology
Springer
2020
Abstract
It is widely supposed that methodological naturalism, understood as a thesis about the methodology of science, is metaphysically neutral, and that this in turn guarantees the value-neutrality of science. In this paper we argue that methodological naturalism is underpinned by certain ontological and epistemological assumptions including evidentialism and the causal closure of the physical, adoption of which necessitates commitment to metaphysical naturalism. © 2019, Springer Nature B.V
Investigating the Effects of DBS on Brain Connectivity by Causal Inference in Parkinson’s Disease
, M.Sc. Thesis Sharif University of Technology ; Rabiee, Hamid Reza (Supervisor)
Abstract
Parkinson’s disease (PD) is a progressive debilitating neurological disorder that causes motor and cognitive impairment. Administration of dopaminergic medication (Levodopa) has been reported to be effective in attenuating the excessive pathological synchronization in basal ganglia. However, long term levodopa therapy has its pitfalls. High frequency deep brain stimulation (DBS) has been suggested as an effective alternative for reducing motor symptoms in PD. In this method, distinct brain regions involved in the pathophysiology of the disease are stimulated electrically at high frequencies (i.e. at 130 Hz). While several studies have been carried out on the effects of DBS and its clinical...
Wideband maximally flat fractional-delay allpass filters
, Article Electronics Letters ; Volume 46, Issue 10 , May , 2010 , Pages 722-723 ; 00135194 (ISSN) ; Sharif University of Technology
2010
Abstract
The maximally flat (MF) fractional-delay (FD) allpass filter, also known as Thiran's allpass filter, is one of the most popular IIR FD systems which is typically deployed in its causal forms. It is shown that if this causality constraint is removed, MFFD allpass filters with considerably wider bandwidths can be obtained. In many applications this extra bandwidth is worth having a non-causal system
Learning linear non-Gaussian causal models in the presence of latent variables
, Article Journal of Machine Learning Research ; Volume 21 , 2020 ; Ghassami, A ; Kiyavash, N ; Zhang, K ; Sharif University of Technology
Microtome Publishing
2020
Abstract
We consider the problem of learning causal models from observational data generated by linear non-Gaussian acyclic causal models with latent variables. Without considering the effect of latent variables, the inferred causal relationships among the observed variables are often wrong. Under faithfulness assumption, we propose a method to check whether there exists a causal path between any two observed variables. From this information, we can obtain the causal order among the observed variables. The next question is whether the causal effects can be uniquely identified as well. We show that causal effects among observed variables cannot be identified uniquely under mere assumptions of...
CuPC: CUDA-Based parallel PC algorithm for causal structure learning on GPU
, Article IEEE Transactions on Parallel and Distributed Systems ; Volume 31, Issue 3 , 2020 , Pages 530-542 ; Jafarinejad, F ; Hashemi, M ; Salehkaleybar, S ; Sharif University of Technology
IEEE Computer Society
2020
Abstract
The main goal in many fields in the empirical sciences is to discover causal relationships among a set of variables from observational data. PC algorithm is one of the promising solutions to learn underlying causal structure by performing a number of conditional independence tests. In this paper, we propose a novel GPU-based parallel algorithm, called cuPC, to execute an order-independent version of PC. The proposed solution has two variants, cuPC-E and cuPC-S, which parallelize PC in two different ways for multivariate normal distribution. Experimental results show the scalability of the proposed algorithms with respect to the number of variables, the number of samples, and different graph...
Essentional Role of Causal Relations in Scientific Explanations (According to Wesely Salmon)
, Ph.D. Dissertation Sharif University of Technology ; Hosseini, Ali (Supervisor)
Abstract
The ambiguity in the meaning of scientific explanation has caused the philosophers to use different criteria for differentiating it from other explanations. One of the most important of these criteria is Salmon’s criterion. According to Wesley Salmon, a scientific explanation is an objective issue and it is far beyond descriptive knowledge which can be found out about the world. The index provided by Salmon for explanation as a scientific interpretation is based on two principle foundations: 1. Statistical relevance 2. Causal relation. It would be a key to understand the world better. In this paper, first, we will argue that the explanation has both objective and subjective components and...
Causal Models in Representation Learning
, M.Sc. Thesis Sharif University of Technology ; Yassaeei Meybodi, Mohammad Hossein (Supervisor)
Abstract
Undoubtedly, machine learning and its practical applications have become essential topics in research and industry. Among these, representation learning holds special importance. The goal in this field is to find low-dimensional and meaningful representations of high-dimensional data that encapsulate essential information. Most existing methods focus only on the statistical correlations in data, which do not necessarily indicate real relationships between variables and can affect learning performance. This becomes even more problematic when the distribution of test data differs from the training data, as it makes the learned representations less generalizable. To tackle this problem, causal...
Investing Parent’s Preferences on Quality and Quantity of Their Children: a Case Study of Iranian Society, from 1385 to 1390
, M.Sc. Thesis Sharif University of Technology ; Fatemi Ardestani, Farshad (Supervisor)
Abstract
This thesis aims to the study of quality-quantity trade off in the family through investigating the effects of family size and birth order on their educational attainment. The latter has been chosen as a criteria for measuring the quality; to measure the effects of quantity we have used the twins for the instrumental variable as an endogenous shock in the family size to reduce bias that happens as a result of omitted variables bias and to measure the effect of family size on it’s quality more accurately. We have also used censored regression to eliminate the bias of student children in the sample.In addition to concentrating on the differences between children of different family sizes we...
A Critical Reading of Russel on Causation
, M.Sc. Thesis Sharif University of Technology ; Taghavi, Mostafa (Supervisor)
Abstract
Causation is one of the most old and difficult problems of Metaphysics which is also important in Science and Philosophy of Science. There are three different views in the Ontology of Causation—Eliminativism, Fundamentalism and Reductionism. Also we know that the Ontology of Causation can be equated with the question of the difference between Causal and Non-Causal series of events, such that the answers to this question are correspondent to the mentioned views: 1)Eliminativism: There is not any difference between those two series and the concept of Causation does not have any objective or external being. 2)Primitivism: There is a difference between them but it can not be explained in any...
Analogies and Differences Between Auantum Mechanics and Quanmtum Field Theory (Causality and Locality)
, M.Sc. Thesis Sharif University of Technology ; Golshani, Mehdi (Supervisor)
Abstract
Causality and locality are two of the fundamental problems in physics and physicists have different interpretations of them. In new physics theories like Quantum mechanics and Quantum field theory, the main point of discussion is that whether these two theories are local and causal or not? In first chapter, we introduce the principle of causality. The second and third chapters cover causality in Quantum mechanics and Quantum field theory.The last chapter is about locality
Cause-and-effect relationships affecting distribution loss management
, Article IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT Europe, 11 October 2010 through 13 October 2010 ; October , 2010 ; 9781424485109 (ISBN) ; Ehsan, M ; Masjed Jamei, M ; Sharif University of Technology
2010
Abstract
There are a lot of parameters which are decisive in distribution loss management, such as demand value, energy price, etc. In this paper the cause-and-effect relationships among some of these variables are clearly described. The study demonstrates how a variable in the model will change, if an alteration in one of the other variables occurs. The power system variables considered in this study include energy and loss prices, generation and demand values, distribution loss, distribution system age, number of customers connected to the smart grid, distribution network revenue, and so forth. The smart grid impacts can be studied from two different aspects: 1) the communication infrastructure...
Learning of Causal Structures with Deep Reinforcement Learning
, M.Sc. Thesis Sharif University of Technology ; Saleh Kaleybar, Saber (Supervisor) ; Hashemi, Matin (Co-Supervisor)
Abstract
We study the problem of experiment design to learn causal structures from interventional data. We consider an active learning setting in which the experimenter decides to intervene on one of the variables in the system in each step and uses the results of the intervention to recover further causal relationships among the variables. The goal is to fully identify the causal structures with minimum number of interventions. We present the first deep reinforcement learning based solution for the problem of experiment design. In the proposed method, we embed input graphs to vectors using a graph neural network and feed them to another neural network which outputs a variable for performing...
Effective Connectivity Analysis in Neural circuitry Underlying Perceptual and Value-based Memory
, M.Sc. Thesis Sharif University of Technology ; Amini, Arash (Supervisor) ; Ghazizadeh, Ali (Co-Supervisor)
Abstract
Perceptual memory used in novel vs familiar discrimination is not only vital for the evaluation of environmental variations but also essential for learning, perception, and correcting behavioral policies. On the other hand, value-based memory which allows for discrimination of valuable objects among equally familiar ones also drives behavioral interactions and decision making. Although many studies have been conducted to address the neuronal association regarding each separately, the neural correspondence between perceptual and value-based memory is not scrutinized adequately. To this end, the differential neural activation in two macaque monkeys to unrewarded novel vs familiar fractals (>100...
Interpretability of Machine Learning Algorithms through the Lens of Causal Inference
, M.Sc. Thesis Sharif University of Technology ; Mohammad Hossein Yassaee (Supervisor)
Abstract
Machine learning is becoming increasingly popular for solving various problems, and it has become a big part of our daily lives. However, with the use of complex machine learning models, it is important to explain how these algorithms work. Knowing why a model makes a certain prediction can be just as important as the accuracy of that prediction in many applications. Unfortunately, the highest accuracies for large data sets are often achieved by complex models that are difficult to interpret even for the designers. Therefore, the interpretability of machine learning algorithms has become just as important as their accuracy. Recently, different methods have been proposed to help users...
Effective connectivity inference in the whole-brain network by using rDCM method for investigating the distinction between emotional states in fMRI data
, Article Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization ; 2022 ; 21681163 (ISSN) ; Ghahari, S ; Fatemizadeh, E ; Motie Nasrabadi, A ; Sharif University of Technology
Taylor and Francis Ltd
2022
Abstract
In recent years, the regression dynamic causal modelling (rDCM) method was introduced as a new version of dynamic causal modelling (DCM) to derive effective connectivity in whole-brain networks for functional magnetic resonance imaging (fMRI) data. In this research, we used data obtained while applying the stimulation of audio movie comprised different emotional states. We applied this method to two networks consisting of ten auditory and forty-four regions, respectively. This method was used to study effective connections between emotional states and represent the distinction between emotions. Finally, significant effective connections were found in emotional processing and auditory...
, M.Sc. Thesis Sharif University of Technology ; Ayatolahy, Hamid Reza (Supervisor)
Abstract
The concept of causality٫continually٫from the seventeenth century٫ As yet has been debated between philosopfers and partly physicists. This diversity of opinions rely on their’s ontologic and epistemologic positions. In the article, is attempted to compare causality in the philosophy of contem -porary(west) and Quantum physics with Islamic Philosophy (according to motahhary) In first chapter, universal hypothesizes and reasons of requirement to analyze causality is expressed. In second chapter,we turn to views of four famous hilosophers in the seventeenth and eiyhteenth century: locke ,Berkeley, Hume, kant. In third chapter, we attend to some current approach the recent philosophers...