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Littérature scientifique sur le sujet « Causal graphs »

Auteur : Grafiati

Publié le 9 mars 2023

Mis à jour le 19 juillet 2025

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Articles de revues sur le sujet "Causal graphs"

Atherton, Juli, Derek Ruths, and Adrian Vetta. "Computation in Causal Graphs." Journal of Graph Algorithms and Applications 23, no. 2 (2019): 317–44. http://dx.doi.org/10.7155/jgaa.00493.

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Lipsky, Ari M., and Sander Greenland. "Causal Directed Acyclic Graphs." JAMA 327, no. 11 (2022): 1083. http://dx.doi.org/10.1001/jama.2022.1816.

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Kischka, Peter, and Dietrich Eherler. "Causal graphs and unconfoundedness." Allgemeines Statistisches Archiv 85, no. 3 (2001): 247–66. http://dx.doi.org/10.1007/s101820100064.

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Kinney, David. "Curie’s principle and causal graphs." Studies in History and Philosophy of Science Part A 87 (June 2021): 22–27. http://dx.doi.org/10.1016/j.shpsa.2021.02.007.

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Gimenez, O., and A. Jonsson. "The Complexity of Planning Problems With Simple Causal Graphs." Journal of Artificial Intelligence Research 31 (February 26, 2008): 319–51. http://dx.doi.org/10.1613/jair.2432.

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We present three new complexity results for classes of planning problems with simple causal graphs. First, we describe a polynomial-time algorithm that uses macros to generate plans for the class 3S of planning problems with binary state variables and acyclic causal graphs. This implies that plan generation may be tractable even when a planning problem has an exponentially long minimal solution. We also prove that the problem of plan existence for planning problems with multi-valued variables and chain causal graphs is NP-hard. Finally, we show that plan existence for planning problems with binary state variables and polytree causal graphs is NP-complete.

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Jonsson, Anders, Peter Jonsson, and Tomas Lööw. "When Acyclicity Is Not Enough: Limitations of the Causal Graph." Proceedings of the International Conference on Automated Planning and Scheduling 23 (June 2, 2013): 117–25. http://dx.doi.org/10.1609/icaps.v23i1.13550.

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Causal graphs are widely used in planning to capture the internal structure of planning instances. In the past, causal graphs have been exploited to generate hierarchical plans, to compute heuristics, and to identify classes of planning instances that are easy to solve. It is generally believed that planning is easier when the causal graph is acyclic. In this paper we show that this is not true in the worst case, proving that the problem of plan existence is PSPACE-complete even when the causal graph is acyclic. Since the variables of the planning instances in our reduction are propositional, this result applies to STRIPS planning with negative pre-conditions. Having established that planning is hard for acyclic causal graphs, we study a subclass of planning instances with acyclic causal graphs whose variables have strongly connected domain transition graphs. For this class, we show that plan existence is easy, but that bounded plan existence is hard, implying that optimal planning is significantly harder than satisficing planning for this class.

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Puente, C., A. Sobrino, J. A. Olivas, and E. Garrido. "Summarizing information by means of causal sentences through causal graphs." Journal of Applied Logic 24 (November 2017): 3–14. http://dx.doi.org/10.1016/j.jal.2016.11.020.

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Wieczorek, Aleksander, and Volker Roth. "Information Theoretic Causal Effect Quantification." Entropy 21, no. 10 (2019): 975. http://dx.doi.org/10.3390/e21100975.

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Modelling causal relationships has become popular across various disciplines. Most common frameworks for causality are the Pearlian causal directed acyclic graphs (DAGs) and the Neyman-Rubin potential outcome framework. In this paper, we propose an information theoretic framework for causal effect quantification. To this end, we formulate a two step causal deduction procedure in the Pearl and Rubin frameworks and introduce its equivalent which uses information theoretic terms only. The first step of the procedure consists of ensuring no confounding or finding an adjustment set with directed information. In the second step, the causal effect is quantified. We subsequently unify previous definitions of directed information present in the literature and clarify the confusion surrounding them. We also motivate using chain graphs for directed information in time series and extend our approach to chain graphs. The proposed approach serves as a translation between causality modelling and information theory.

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Levine, Eli, and J. Butler. "Causal Graphs and Concept-Mapping Assumptions." Applied System Innovation 1, no. 3 (2018): 25. http://dx.doi.org/10.3390/asi1030025.

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Determining what constitutes a causal relationship between two or more concepts, and how to infer causation, are fundamental concepts in statistics and all the sciences. Causation becomes especially difficult in the social sciences where there is a myriad of different factors that are not always easily observed or measured that directly or indirectly influence the dynamic relationships between independent variables and dependent variables. This paper proposes a procedure for helping researchers explicitly understand what their underlying assumptions are, what kind of data and methodology are needed to understand a given relationship, and how to develop explicit assumptions with clear alternatives, such that researchers can then apply a process of probabilistic elimination. The procedure borrows from Pearl’s concept of “causal diagrams” and concept mapping to create a repeatable, step-by-step process for systematically researching complex relationships and, more generally, complex systems. The significance of this methodology is that it can help researchers determine what is more probably accurate and what is less probably accurate in a comprehensive fashion for complex phenomena. This can help resolve many of our current and future political and policy debates by eliminating that which has no evidence in support of it, and that which has evidence against it, from the pool of what can be permitted in research and debates. By defining and streamlining a process for inferring truth in a way that is graspable by human cognition, we can begin to have more productive and effective discussions around political and policy questions.

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Chindelevitch, Leonid, Po-Ru Loh, Ahmed Enayetallah, Bonnie Berger, and Daniel Ziemek. "Assessing statistical significance in causal graphs." BMC Bioinformatics 13, no. 1 (2012): 35. http://dx.doi.org/10.1186/1471-2105-13-35.

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Thèses sur le sujet "Causal graphs"

Simonne, Lucas. "Mining differential causal rules in knowledge graphs." Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASG008.

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La fouille de règles d'association au sein de graphes de connaissances est un domaine de recherche important.En effet, ce type de règle permet de représenter des connaissances, et leur application permet de compléter un graphe en ajoutant des données manquantes ou de supprimer des données erronées.Cependant, ces règles ne permettent pas d'exprimer des relations causales, dont la sémantique diffère d'une simple association ou corrélation. Dans un système, un lien de causalité entre une variable A et une variable B est une relation orientée de A vers B et indique qu'un changement dans A cause un changement dans B, les autres variables du système conservant les mêmes valeurs.Plusieurs cadres d'étude existent pour déterminer des relations causales, dont le modèle d'étude des résultats potentiels, qui consiste à apparier des instances similaires ayant des valeurs différentes sur une variable nommée traitement pour étudier l'effet de ce traitement sur une autre variable nommée résultat.Nous proposons dans cette thèse plusieurs approches permettant de définir des règles représentant l'effet causal d'un traitement sur un résultat.Cet effet peut être local, i.e., valide pour un sous-ensemble d'instances d'un graphe de connaissances défini par un motif de graphe, ou bien moyen, i.e., valide en moyenne pour l'ensemble d'instances de la classe considérée. La découverte de ces règles se base sur le cadre d'étude des résultats potentiels en appariant des instances similaires, en comparant leurs descriptions RDF au sein du graphe ou bien leurs représentations vectorielles apprises à travers des modèles de plongements de graphes The mining of association rules within knowledge graphs is an important area of research.Indeed, this type of rule makes it possible to represent knowledge, and their application makes it possible to complete a knowledge graph by adding missing triples or to remove erroneous triples.However, these rules express associations and do not allow the expression of causal relations, whose semantics differ from an association or a correlation.In a system, a causal link between variable A and variable B is a relationship oriented from A to B. It indicates that a change in A causes a change in B, with the other variables in the system maintaining the same values.Several frameworks exist for determining causal relationships, including the potential outcome framework, which involves matching similar instances with different values on a variable named treatment to study the effect of that treatment on another variable named the outcome.In this thesis, we propose several approaches to define rules representing a causal effect of a treatment on an outcome.This effect can be local, i.e., valid for a subset of instances of a knowledge graph defined by a graph pattern, or average, i.e., valid on average for the whole set of graph instances.The discovery of these rules is based on the framework of studying potential outcomes by matching similar instances and comparing their RDF descriptions or their learned vectorial representations through graph embedding models

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Choudhry, Arjun. "Narrative Generation to Support Causal Exploration of Directed Graphs." Thesis, Virginia Tech, 2020. http://hdl.handle.net/10919/98670.

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Causal graphs are a useful notation to represent the interplay between the actors as well as the polarity and strength of the relationship that they share. They are used extensively in educational, professional, and industrial contexts to simulate different scenarios, validate behavioral aspects, visualize the connections between different processes, and explore the adversarial effects of changing certain nodes. However, as the size of the causal graphs increase, interpreting them also becomes increasingly tougher. In such cases, new analytical tools are required to enhance the user's comprehension of the graph, both in terms of correctness and speed. To this purpose, this thesis introduces 1) a system that allows for causal exploration of directed graphs, while enabling the user to see the effect of interventions on the target nodes, 2) the use of natural language generation techniques to create a coherent passage explaining the propagation effects, and 3) results of an expert user study validating the efficacy of the narratives in enhancing the user's understanding of the causal graphs. In overall, the system aims to enhance user experience and promote further causal exploration. Master of Science Narrative generation is the art of creating coherent snippets of text that cumulatively describe a succession of events, played across a period of time. These goals of narrative generation are also shared by causal graphs – models that encapsulate inferences between the nodes through the strength and polarity of the connecting edges. Causal graphs are an useful mechanism to visualize changes propagating amongst nodes in the system. However, as the graph starts addressing real-world actors and their interactions, it becomes increasingly difficult to understand causal inferences between distant nodes, especially if the graph is cyclic. Moreover, if the value of more than a single node is altered and the cumulative effect of the change is to be perceived on a set of target nodes, it becomes extremely difficult to the human eye. This thesis attempts to alleviate this problem by generating dynamic narratives detailing the effect of one or more interventions on one or more target nodes, incorporating time-series analysis, Wikification, and spike detection. Moreover, the narrative enhances the user's understanding of the change propagation occurring in the system. The efficacy of the narrative was further corroborated by the results of user studies, which concluded that the presence of the narrative aids the user's confidence level, correctness, and speed while exploring the causal network.

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Bernigau, Holger. "Causal Models over Infinite Graphs and their Application to the Sensorimotor Loop." Doctoral thesis, Universitätsbibliothek Leipzig, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-164734.

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Motivation and background The enormous amount of capabilities that every human learns throughout his life, is probably among the most remarkable and fascinating aspects of life. Learning has therefore drawn lots of interest from scientists working in very different fields like philosophy, biology, sociology, educational sciences, computer sciences and mathematics. This thesis focuses on the information theoretical and mathematical aspects of learning. We are interested in the learning process of an agent (which can be for example a human, an animal, a robot, an economical institution or a state) that interacts with its environment. Common models for this interaction are Markov decision processes (MDPs) and partially observable Markov decision processes (POMDPs). Learning is then considered to be the maximization of the expectation of a predefined reward function. In order to formulate general principles (like a formal definition of curiosity-driven learning or avoidance of unpleasant situation) in a rigorous way, it might be desirable to have a theoretical framework for the optimization of more complex functionals of the underlying process law. This might include the entropy of certain sensor values or their mutual information. An optimization of the latter quantity (also known as predictive information) has been investigated intensively both theoretically and experimentally using computer simulations by N. Ay, R. Der, K Zahedi and G. Martius. In this thesis, we develop a mathematical theory for learning in the sensorimotor loop beyond expected reward maximization. Approaches and results This thesis covers four different topics related to the theory of learning in the sensorimotor loop. First of all, we need to specify the model of an agent interacting with the environment, either with learning or without learning. This interaction naturally results in complex causal dependencies. Since we are interested in asymptotic properties of learning algorithms, it is necessary to consider infinite time horizons. It turns out that the well-understood theory of causal networks known from the machine learning literature is not powerful enough for our purpose. Therefore we extend important theorems on causal networks to infinite graphs and general state spaces using analytical methods from measure theoretic probability theory and the theory of discrete time stochastic processes. Furthermore, we prove a generalization of the strong Markov property from Markov processes to infinite causal networks. Secondly, we develop a new idea for a projected stochastic constraint optimization algorithm. Generally a discrete gradient ascent algorithm can be used to generate an iterative sequence that converges to the stationary points of a given optimization problem. Whenever the optimization takes place over a compact subset of a vector space, it is possible that the iterative sequence leaves the constraint set. One possibility to cope with this problem is to project all points to the constraint set using Euclidean best-approximation. The latter is sometimes difficult to calculate. A concrete example is an optimization over the unit ball in a matrix space equipped with operator norm. Our idea consists of a back-projection using quasi-projectors different from the Euclidean best-approximation. In the matrix example, there is another canonical way to force the iterative sequence to stay in the constraint set: Whenever a point leaves the unit ball, it is divided by its norm. For a given target function, this procedure might introduce spurious stationary points on the boundary. We show that this problem can be circumvented by using a gradient that is tailored to the quasi-projector used for back-projection. We state a general technical compatibility condition between a quasi-projector and a metric used for gradient ascent, prove convergence of stochastic iterative sequences and provide an appropriate metric for the unit-ball example. Thirdly, a class of learning problems in the sensorimotor loop is defined and motivated. This class of problems is more general than the usual expected reward maximization and is illustrated by numerous examples (like expected reward maximization, maximization of the predictive information, maximization of the entropy and minimization of the variance of a given reward function). We also provide stationarity conditions together with appropriate gradient formulas. Last but not least, we prove convergence of a stochastic optimization algorithm (as considered in the second topic) applied to a general learning problem (as considered in the third topic). It is shown that the learning algorithm converges to the set of stationary points. Among others, the proof covers the convergence of an improved version of an algorithm for the maximization of the predictive information as proposed by N. Ay, R. Der and K. Zahedi. We also investigate an application to a linear Gaussian dynamic, where the policies are encoded by the unit-ball in a space of matrices equipped with operator norm.

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Yang, Karren Dai. "Learning causal graphs under interventions and applications to single-cell biological data analysis." Thesis, Massachusetts Institute of Technology, 2021. https://hdl.handle.net/1721.1/130806.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Biological Engineering, February, 2021 Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2021 Cataloged from the official PDF version of thesis. Includes bibliographical references (pages 49-51). This thesis studies the problem of learning causal directed acyclic graphs (DAGs) in the setting where both observational and interventional data is available. This setting is common in biology, where gene regulatory networks can be intervened on using chemical reagents or gene deletions. The identifiability of causal DAGs under perfect interventions, which eliminate dependencies between targeted variables and their direct causes, has previously been studied. This thesis first extends these identifiability results to general interventions, which may modify the dependencies between targeted variables and their causes without eliminating them, by defining and characterizing the interventional Markov equivalence class that can be identified from general interventions. Subsequently, this thesis proposes the first provably consistent algorithm for learning DAGs in this setting. Finally, this algorithm as well as related work is applied to analyze biological datasets. by Karren Dai Yang. S.M. S.M. S.M. Massachusetts Institute of Technology, Department of Biological Engineering S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science

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Cabeli, Vincent. "Learning causal graphs from continuous or mixed datasets of biological or clinical interest." Electronic Thesis or Diss., Sorbonne université, 2021. http://www.theses.fr/2021SORUS331.

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Les travaux de cette thèse s’inscrivent dans la théorie principalement développée par Judea Pearl sur les diagrammes causaux; des modèles graphiques qui permettent de dériver toutes les quantités causales d’intérêt formellement et intuitivement. Nous traitons le problème de l’inférence de réseau causal à partir uniquement de données d’observation, c’est-à-dire sans aucune intervention de la part de l’expérimentateur. En particulier, nous proposons d’améliorer les méthodes existantes pour les rendre plus aptes à analyser des données issues du monde réel, en nous affranchissant le plus possible des contraintes sur les distributions des données, et en les rendant plus interprétables. Nous proposons une extension de MIIC, une approche basée sur les contraintes et la théorie de l’information pour retrouver la classe d’équivalence du graphe causal à partir d’observations. Notre contribution est un algorithme de discrétisation optimale basé sur le principe de description minimale pour simultanément estimer la valeur de l’information mutuelle (et multivariée) et évaluer sa significativité entre des échantillons de variables de n’importe quelle nature : continue, catégorique ou mixte. Nous mettons à profit ces développements pour analyser des jeux de données mixtes d'intérêt clinique (dossiers médicaux de patients atteints de troubles cognitifs; ou du cancer du sein) ou biologique (réseaux de régulation génique de cellules précurseur hématopoïétiques) The work in this thesis follows the theory primarily developed by Judea Pearl on causal diagrams; graphical models that allow all causal quantities of interest to be derived formally and intuitively. We address the problem of causal network inference from observational data alone, i.e., without any intervention from the experimenter. In particular, we propose to improve existing methods to make them more suitable for analyzing real-world data, by freeing them as much as possible from constraints on data distributions, and by making them more interpretable. We propose an extension of MIIC, a constraint-based information-theoretic approach to recover the equivalence class of the causal graph from observations. Our contribution is an optimal discretization algorithm based on the minimum description length principle to simultaneously estimate the value of mutual (and multivariate) information and evaluate its significance between samples of variables of any nature: continuous, categorical or mixed. We use these developments to analyze mixed datasets of clinical (medical records of patients with cognitive disorders; or breast cancer and being treated by neoadjuvant chemotherapy) or biological interest (gene regulation networks of hematopoietic stem and precursor cells)

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Giasemidis, Georgios. "Spectral dimension in graph models of causal quantum gravity." Thesis, University of Oxford, 2013. http://ora.ox.ac.uk/objects/uuid:d0aaa6f2-dd0b-4ea9-81c1-7c9e81a7229e.

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The phenomenon of scale dependent spectral dimension has attracted special interest in the quantum gravity community over the last eight years. It was first observed in computer simulations of the causal dynamical triangulation (CDT) approach to quantum gravity and refers to the reduction of the spectral dimension from 4 at classical scales to 2 at short distances. Thereafter several authors confirmed a similar result from different approaches to quantum gravity. Despite the contribution from different approaches, no analytical model was proposed to explain the numerical results as the continuum limit of CDT. In this thesis we introduce graph ensembles as toy models of CDT and show that both the continuum limit and a scale dependent spectral dimension can be defined rigorously. First we focus on a simple graph ensemble, the random comb. It does not have any dynamics from the gravity point of view, but serves as an instructive toy model to introduce the characteristic scale of the graph, study the continuum limit and define the scale dependent spectral dimension. Having defined the continuum limit, we study the reduction of the spectral dimension on more realistic toy models, the multigraph ensembles, which serve as a radial approximation of CDT. We focus on the (recurrent) multigraph approximation of the two-dimensional CDT whose ensemble measure is analytically controlled. The latter comes from the critical Galton-Watson process conditioned on non-extinction. Next we turn our attention to transient multigraph ensembles, corresponding to higher-dimensional CDT. Firstly we study their fractal properties and secondly calculate the scale dependent spectral dimension and compare it to computer simulations. We comment further on the relation between Horava-Lifshitz gravity, asymptotic safety, multifractional spacetimes and CDT-like models.

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CALIGARIS, SILVIA. "A Causal Graphs - based approach for assessing gender disparities: an application to child health & nutrition in China." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2014. http://hdl.handle.net/10281/83241.

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Most of gender statistical measures proposed in the last decades are in fact composite indicators, i.e. weighted linear combinations of basic statistics such as ratios, percentages etc. Composite indicators then involves several arbitrary choices -for instance the weighting/aggregating system, variables selection, standardization affecting both indexes transparency and interpretation. Furthermore gender inequality is a complex latent phenomenon, a collection of disparate and inter-linked issues that can be hardly caught in a single indicator. The development of statistical tools and ad hoc models is then required. The aim of this work is to explore the potential of graphical models as a language able to clearly represent the complex relationships among variables involved in the statistical measuring the gender disparities. In particular we will focus on causal graphs allowing to deep and interpret the causal mechanism that may originate gender gaps as well as to explore the effects of gender tailored policies. Causal models indeed provide transparent mathematical tools to formulate the assumptions underlying all causal inference, to translate them in term of joint distribution and to read off the conditional independences using the d-separation criterion (Pearl 2000). It is thus possible deriving causal effects in non-experimental studies, representing policies’ effects and interventions through the do operator, controlling confounders and interpreting counterfactuals. We show the potential of such models through an application to real data from China Health and Nutrition Survey 2011 ; in particular we explore the eventual existence of gender discrimination in children’ nutrition and health as possible indicator of preference for sons. The analysis takes in exam socio-demographic, economical as well as biological variables. Resorting to the PC algorithm and the IDA algorithm, we aim to learn the underlying causal structure and to estimate causal effect of siblings on children’ nutrition from observational data.

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Bernigau, Holger [Verfasser], Nihat [Akademischer Betreuer] Ay, Nihat [Gutachter] Ay, and Shun-ichi [Gutachter] Amari. "Causal Models over Infinite Graphs and their Application to the Sensorimotor Loop : Causal Models over Infinite Graphs and their Application to theSensorimotor Loop / Holger Bernigau ; Gutachter: Nihat Ay, Shun-ichi Amari ; Betreuer: Nihat Ay." Leipzig : Universitätsbibliothek Leipzig, 2015. http://d-nb.info/1239565127/34.

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Chong, Hogun. "A causal model of linkages among strategy, structure, and performance using directed acyclic graphs: A manufacturing subset of Fortune 500 industrials 1990-1998." Texas A&M University, 2003. http://hdl.handle.net/1969.1/58.

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This research explored the causal relationships among strategies, corporate structure, and performance of the largest U.S. non-financial firms using Directed Acyclic Graphs (DAGs). Corporate strategies and structure have been analyzed as major variables to influence corporate performance in management and organizational studies. However, their causal relationships in terms of which variables are leaders and followers, as well as the choices of variables to configure them, are controversial. Finding of causal relationships among strategic variables, structural variables, and corporate performance is beneficial to researchers as well as corporate mangers. It provides guidance to researchers how to build a model in order to measure influences from one variable to the other, lowering the risk of drawing spurious conclusions. It also provides managers a prospect of how certain important variables would change by making a certain strategic decision. Literatures from agency theory, transactional cost economics, and traditional strategic management perspective are used to suggest variables essential to analyze corporate performance. This study includes size and multi-organizational ownership hierarchy as variables to configure corporate structure. The variables to configure corporate strategies are unrelated and related diversification, ownership by institutional investors, debt, investment in R&D, and investment in advertisement. The study finds that most of the variables classified as corporate strategy and corporate structure variables are either direct or indirect causes of corporate accounting performance. Generally, results supports the relational model: corporate structure® corporate strategy® corporate performance. Ownership hierarchy structure, unrelated diversification, advertising expenses, and R&D intensity have direct causal influences on corporate accounting performance. Size and related diversification affected corporate accounting performance indirectly, both through ownership hierarchy structure. Theoretical causal relationships from agency theory are less supported than those from transaction cost economics and traditional strategic management perspective. Further my study suggests that, in general, good corporate performance in 1990s was mainly achieved by internal expansion through investment in R&D and advertisement, rather than external expansion of firms through unrelated diversification, related diversification, and expansion of ownership hierarchy.

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Aka, Niels Mariano [Verfasser]. "Three Essays on Model Selection in Time Series Econometrics : Model Averaging, Causal Graphs, and Structural Identification / Niels Mariano Aka." Berlin : Freie Universität Berlin, 2021. http://d-nb.info/1229436685/34.

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Livres sur le sujet "Causal graphs"

Yao, Qing. Directed acyclic graphs, linear recursive regression, and inference about causal ordering. University of Toronto, Dept. of Statistics, 1993.

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Isakov, Vladimir. Speak the language of schemes. INFRA-M Academic Publishing LLC., 2022. http://dx.doi.org/10.12737/1860649.

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Schematization and visualization are the necessary means to ensure the activity of a modern specialist. Schematization allows you to highlight the main thing in an object, to discover its constituent elements, to show their relationship, gives impetus to the construction of conceptual approaches. Visualization "dresses" schematic concepts in a bright, expressive artistic and graphic form. The handbook provides descriptions of the most popular means of analytical graphics - maps, graphs, tables, graphs, diagrams, flowcharts (algorithms), chronolents, maps, methodological schemes, etc. The ways of using schemes for analyzing goals, causes, problems, versions are considered. A thematic dictionary of terms and definitions, a "hot twenty" useful schematization are given. For students, undergraduates, postgraduates, teachers of law schools and faculties, as well as for representatives of other specialties - everyone who draws diagrams and works with them.

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Ruiz, Dana Catharine De. La Causa: The Migrant Farmworkers' Story. Raintree Steck-Vaughn, 1993.

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Murray, Myles N. William Murray, Esq.: Land agent in the Illinois Territory before the Revolutionary War. T. Gaus, 1987.

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Ghere, David L. Causes of the American Revolution: Focus on Boston : a unit of study for grades 7-12 / David L. Ghere, Jan F. Spreeman. Organization of American Historians : National Center for History in the Schools, 1998.

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Moneysmith, Marie. Grasas que engordan, grasas que curan: Conozca la diferencia entre las grasas que le hacen bien y las que le pueden causar enfermedades. Panorama Editorial, 2008.

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Sarmiento, Laura Judith Sandoval. Cuadernos tipográficos: Estudios y prácticas en torno a la tipografía en el suroccidente colombiano 2014-2016. Editorial Universidad del Cauca, 2017.

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Varlamov, Oleg. Mivar databases and rules. INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1508665.

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The multidimensional open epistemological active network MOGAN is the basis for the transition to a qualitatively new level of creating logical artificial intelligence. Mivar databases and rules became the foundation for the creation of MOGAN. The results of the analysis and generalization of data representation structures of various data models are presented: from relational to "Entity — Relationship" (ER-model). On the basis of this generalization, a new model of data and rules is created: the mivar information space "Thing-Property-Relation". The logic-computational processing of data in this new model of data and rules is shown, which has linear computational complexity relative to the number of rules. MOGAN is a development of Rule - Based Systems and allows you to quickly and easily design algorithms and work with logical reasoning in the "If..., Then..." format. An example of creating a mivar expert system for solving problems in the model area "Geometry"is given. Mivar databases and rules can be used to model cause-and-effect relationships in different subject areas and to create knowledge bases of new-generation applied artificial intelligence systems and real-time mivar expert systems with the transition to"Big Knowledge".  The textbook in the field of training "Computer Science and Computer Engineering" is intended for students, bachelors, undergraduates, postgraduates studying artificial intelligence methods used in information processing and management systems, as well as for users and specialists who create mivar knowledge models, expert systems, automated control systems and decision support systems.  Keywords: cybernetics, artificial intelligence, mivar, mivar networks, databases, data models, expert system, intelligent systems, multidimensional open epistemological active network, MOGAN, MIPRA, KESMI, Wi!Mi, Razumator, knowledge bases, knowledge graphs, knowledge networks, Big knowledge, products, logical inference, decision support systems, decision-making systems, autonomous robots, recommendation systems, universal knowledge tools, expert system designers, logical artificial intelligence.

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Boller, David. Die letzten Tage der Menschheit: Eine Graphic Novel nach Karl Kraus. Edited by Pietsch Reinhard adapter editor and Kraus Karl 1874-1936. Herbert Utz Verlag GmbH, 2014.

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Chapitres de livres sur le sujet "Causal graphs"

Dayal, Vikram, and Anand Murugesan. "Causal Graphs." In Demystifying Causal Inference. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3905-3_5.

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Ma, Jing, Ruocheng Guo, and Jundong Li. "Causal Inference on Graphs." In Machine Learning for Causal Inference. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-35051-1_4.

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Brumback, Babette A. "Causal Directed Acyclic Graphs." In Fundamentals of Causal Inference with R. Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003146674-5.

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Foraita, Ronja, Jacob Spallek, and Hajo Zeeb. "Causal Directed Acyclic Graphs." In Handbook of Epidemiology. Springer New York, 2023. http://dx.doi.org/10.1007/978-1-4614-6625-3_65-1.

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Águeda, Cristina Puente. "Causal Relations, Text Mining and Causal Graphs." In Accuracy and Fuzziness. A Life in Science and Politics. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18606-1_2.

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Jensen, Finn V. "Causal and Bayesian Networks." In Bayesian Networks and Decision Graphs. Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4757-3502-4_1.

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Gebharter, Alexander, and Marie I. Kaiser. "Causal Graphs and Biological Mechanisms." In Explanation in the Special Sciences. Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-7563-3_3.

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Pearl, J. "Statistics, Causality, and Graphs." In Causal Models and Intelligent Data Management. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-58648-4_1.

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Mohan, Karthika. "Causal Graphs for Missing Data: A Gentle Introduction." In Probabilistic and Causal Inference. ACM, 2022. http://dx.doi.org/10.1145/3501714.3501750.

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Eherler, Dietrich, and Peter Kischka. "Decision Making Based on Causal Graphs." In Models, Methods and Decision Support for Management. Physica-Verlag HD, 2001. http://dx.doi.org/10.1007/978-3-642-57603-4_18.

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Actes de conférences sur le sujet "Causal graphs"

Russo, Fabrizio, Anna Rapberger, and Francesca Toni. "Argumentative Causal Discovery." In 21st International Conference on Principles of Knowledge Representation and Reasoning {KR-2023}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/kr.2024/88.

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Causal discovery amounts to unearthing causal relationships amongst features in data. It is a crucial companion to causal inference, necessary to build scientific knowledge without resorting to expensive or impossible randomised control trials. In this paper, we explore how reasoning with symbolic representations can support causal discovery. Specifically, we deploy assumption-based argumentation (ABA), a well-established and powerful knowledge representation formalism, in combination with causality theories, to learn graphs which reflect causal dependencies in the data. We prove that our method exhibits desirable properties, notably that, under natural conditions, it can retrieve ground-truth causal graphs. We also conduct experiments with an implementation of our method in answer set programming (ASP) on four datasets from standard benchmarks in causal discovery, showing that our method compares well against established baselines.

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Chen, Sirui, Mengying Xu, Kun Wang, et al. "CLEAR: Can Language Models Really Understand Causal Graphs?" In Findings of the Association for Computational Linguistics: EMNLP 2024. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.findings-emnlp.363.

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Mo, Zhaobin, Qingyuan Liu, Baohua Yan, Longxiang Zhang, and Xuan Di. "Causal Adjacency Learning for Spatiotemporal Prediction Over Graphs." In 2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2024. https://doi.org/10.1109/itsc58415.2024.10920273.

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Gholami, Mohammadreza, Mahshad Faramarzi, Nima Alipour, and Mansooreh Pakravan. "Node Embedding Extraction for Causal Brain Graphs in fMRI Data." In 2025 29th International Computer Conference, Computer Society of Iran (CSICC). IEEE, 2025. https://doi.org/10.1109/csicc65765.2025.10967434.

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Zhu, Jiageng, Kehao Li, Zheda Mai, Hanchen Xie, Wael AbdAlmageed, and Zubin Abraham. "Attention-Driven Causal Discovery: From Transformer Matrices to Granger Causal Graphs for Non-Stationary Time-series Data." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10889219.

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Chou, Jingyuan, Jiangzhuo Chen, and Madhav Marathe. "Estimate Causal Effects of Entangled Treatment on Graphs using Disentangled Instrumental Variables." In 2024 IEEE International Conference on Big Data (BigData). IEEE, 2024. https://doi.org/10.1109/bigdata62323.2024.10825713.

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Tan, Fiona Anting, Jay Desai, and Srinivasan H. Sengamedu. "Enhancing Fact Verification with Causal Knowledge Graphs and Transformer-Based Retrieval for Deductive Reasoning." In Proceedings of the Seventh Fact Extraction and VERification Workshop (FEVER). Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.fever-1.20.

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Sun, Wenjing. "Exploiting the Benefits of Popularity Bias Based on Causal Graphs in Optimizing Recommender Systems." In 2024 IEEE 4th International Conference on Software Engineering and Artificial Intelligence (SEAI). IEEE, 2024. http://dx.doi.org/10.1109/seai62072.2024.10674557.

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Chou, Jingyuan, Jiangzhuo Chen, and Madhav Marathe. "State-Of-The-Art and Challenges in Causal Inference on Graphs: Confounders and Interferences." In 2024 IEEE 6th International Conference on Cognitive Machine Intelligence (CogMI). IEEE, 2024. https://doi.org/10.1109/cogmi62246.2024.00011.

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Tonon, Andrea, Meng Zhang, Bora Caglayan, et al. "RADICE: Causal Graph Based Root Cause Analysis for System Performance Diagnostic." In 2025 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER). IEEE, 2025. https://doi.org/10.1109/saner64311.2025.00050.

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Rapports d'organisations sur le sujet "Causal graphs"

Turgeon, Mathieu. Causal Modeling with Regression Discontinuity Designs (RDD). Instats Inc., 2023. http://dx.doi.org/10.61700/s3nl5lfnmruqw469.

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This seminar introduces the use of the regression discontinuity design (RDD) to estimate treatment effects from observational data. Day 1 topics include directed acyclic graphs (DAG), potential outcomes framework and associated assumptions, the sharp regression discontinuity design, and tools for visualizing discontinuities. Day 2 topics focus on the estimation of values of interest from a regression discontinuity design, adopting a continuity-based approach to RD analysis. Day 2 will also cover issues related to the validation and falsification of the regression discontinuity design. An official Instats certificate of completion is provided at the conclusion of the seminar. For European PhD students, each seminar offers 2 ECTS Equivalent points.

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Turgeon, Mathieu. Causal Modeling with Regression Discontinuity Designs (RDD). Instats Inc., 2023. http://dx.doi.org/10.61700/o6e22r1sh4h7m469.

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This seminar introduces the use of the regression discontinuity design (RDD) to estimate treatment effects from observational data. Sessions 1-3 topics include directed acyclic graphs (DAG), potential outcomes framework and associated assumptions, the sharp regression discontinuity design, and tools for visualizing discontinuities. Sessions 4-6 topics focus on the estimation of values of interest from a regression discontinuity design, adopting a continuity-based approach to RD analysis. Day 2 will also cover issues related to the validation and falsification of the regression discontinuity design. An official Instats certificate of completion is provided at the conclusion of the seminar. For European PhD students, each seminar offers 2 ECTS Equivalent points.

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Naugle, Asmeret, Laura Swiler, Kiran Lakkaraju, Stephen Verzi, Christina Warrender, and Vicente Romero. Graph-Based Similarity Metrics for Comparing Simulation Model Causal Structures. Office of Scientific and Technical Information (OSTI), 2022. http://dx.doi.org/10.2172/1884926.

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Castleman, Benjamin, and Bridget Terry Long. Looking Beyond Enrollment: The Causal Effect of Need-Based Grants on College Access, Persistence, and Graduation. National Bureau of Economic Research, 2013. http://dx.doi.org/10.3386/w19306.

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Lichter, Amnon, Joseph L. Smilanick, Dennis A. Margosan, and Susan Lurie. Ethanol for postharvest decay control of table grapes: application and mode of action. United States Department of Agriculture, 2005. http://dx.doi.org/10.32747/2005.7587217.bard.

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Original objectives: Dipping of table grapes in ethanol was determined to be an effective measure to control postharvest gray mold infection caused by Botrytis cinerea. Our objectives were to study the effects of ethanol on B.cinerea and table grapes and to conduct research that will facilitate the implementation of this treatment. Background: Botrytis cinerea is known as the major pathogen of table grapes in cold storage. To date, the only commercial technology to control it relied on sulfur dioxide (SO₂) implemented by either fumigation of storage facilities or from slow release generator pads which are positioned directly over the fruits. This treatment is very effective but it has several drawbacks such as aftertaste, bleaching and hypersensitivity to humans which took it out of the GRAS list of compounds and warranted further seek for alternatives. Prior to this research ethanol was shown to control several pathogens in different commodities including table grapes and B. cinerea. Hence it seemed to be a simple and promising technology which could offer a true alternative for storage of table grapes. Further research was however required to answer some practical and theoretical questions which remained unanswered. Major conclusions, solutions, achievements: In this research project we have shown convincingly that 30% ethanol is sufficient to prevent germination of B. cinerea and kill the spores. In a comparative study it was shown that Alternaria alternata is also rather sensitive but Rhizopus stolonifer and Aspergillus niger are less sensitive to ethanol. Consequently, ethanol protected the grapes from decay but did not have a significant effect on occurrence of mycotoxigenic Aspergillus species which are present on the surface of the berry. B. cinerea responded to ethanol or heat treatments by inducing sporulation and transient expression of the heat shock protein HSP104. Similar responses were not detected in grape berries. It was also shown that application of ethanol to berries did not induce subsequent resistance and actually the berries were slightly more susceptible to infection. The heat dose required to kill the spores was determined and it was proven that a combination of heat and ethanol allowed reduction of both the ethanol and heat dose. Ethanol and heat did not reduce the amount or appearance of the wax layers which are an essential component of the external protection of the berry. The ethanol and acetaldehyde content increased after treatment and during storage but the content was much lower than the natural ethanol content in other fruits. The efficacy of ethanol applied before harvest was similar to that of the biological control agent, Metschnikowia fructicola, Finally, the performance of ethanol could be improved synergistically by packaging the bunches in modified atmosphere films which prevent the accumulation of free water. Implications, both scientific and agricultural: It was shown that the major mode of action of ethanol is mediated by its lethal effect on fungal inoculum. Because ethanol acts mainly on the cell membranes, it was possible to enhance its effect by lowering the concentration and elevating the temperature of the treatment. Another important development was the continuous protection of the treated bunches by modified atmosphere that can solve the problem of secondary or internal infection. From the practical standpoint, a variety of means were offered to enhance the effect of the treatment and to offer a viable alternative to SO2 which could be instantly adopted by the industry with a special benefit to growers of organic grapes.

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Juden, Matthew, Tichaona Mapuwei, Till Tietz, et al. Process Outcome Integration with Theory (POInT): academic report. Centre for Excellence and Development Impact and Learning (CEDIL), 2023. http://dx.doi.org/10.51744/crpp5.

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This paper describes the development and testing of a novel approach to evaluating development interventions – the POInT approach. The authors used Bayesian causal modelling to integrate process and outcome data to generate insights about all aspects of the theory of change, including outcomes, mechanisms, mediators and moderators. They partnered with two teams who had evaluated or were evaluating complex development interventions: The UPAVAN team had evaluated a nutrition-sensitive agriculture intervention in Odisha, India, and the DIG team was in the process of evaluating a disability-inclusive poverty graduation intervention in Uganda. The partner teams’ theory of change were adapted into a formal causal model, depicted as a directed acyclic graph (DAG). The DAG was specified in the statistical software R, using the CausalQueries package, having extended the package to handle large models. Using a novel prior elicitation strategy to elicit beliefs over many more parameters than has previously been possible, the partner teams’ beliefs about the nature and strength of causal links in the causal model (priors) were elicited and combined into a single set of shared prior beliefs. The model was updated on data alone as well as on data plus priors to generate posterior models under different assumptions. Finally, the prior and posterior models were queried to learn about estimates of interest, and the relative role of prior beliefs and data in the combined analysis.

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Reisch, Bruce, Avichai Perl, Julie Kikkert, Ruth Ben-Arie, and Rachel Gollop. Use of Anti-Fungal Gene Synergisms for Improved Foliar and Fruit Disease Tolerance in Transgenic Grapes. United States Department of Agriculture, 2002. http://dx.doi.org/10.32747/2002.7575292.bard.

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Original objectives . 1. Test anti-fungal gene products for activity against Uncinula necator, Aspergillus niger, Rhizopus stolonifer and Botrytis cinerea. 2. For Agrobacterium transformation, design appropriate vectors with gene combinations. 3. Use biolistic bombardment and Agrobacterium for transformation of important cultivars. 4. Characterize gene expression in transformants, as well as level of powdery mildew and Botrytis resistance in foliage of transformed plants. Background The production of new grape cultivars by conventional breeding is a complex and time-consuming process. Transferring individual traits via single genes into elite cultivars was proposed as a viable strategy, especially for vegetatively propagated crops such as grapevines. The availability of effective genetic transformation procedures, the existence of genes able to reduce pathogen stress, and improved in vitro culture methods for grapes, were combined to serve the objective of this proposal. Effective deployment of resistance genes would reduce production costs and increase crop quality, and several such genes and combinations were used in this project. Progress The efficacy of two-way combinations of Trichoderma endochitinase (CHIT42), synthetic peptide ESF12 and resveratrol upon the control of growth of Botrytis cinerea and Penicillium digitatum were evaluated in vitro. All pairwise interactions were additive but not synergistic. Per objective 2, suitable vectors with important gene combinations for Agrobacterium transformation were designed. In addition, multiple gene co-transformation by particle bombardment was also tested successfully. In New York, transformation work focused on cultivars Chardonnay and Merlot, while the technology in Israel was extended to 41B, R. 110, Prime, Italia, Gamay, Chardonnay and Velika. Transgenic plant production is summarized in the appendix. Among plants developed in Israel, endochitinase expression was assayed via the MuchT assay using material just 1-5 days after co-cultivation. Plants of cv. Sugraone carrying the gene coding for ESF12, a short anti-fungal lytic peptide under the control of the double 358 promoter, were produced. Leaf extracts of two plants showed inhibition zones that developed within 48 h indicating the inhibitory effect of the leaf extracts on the six species of bacteria. X fastidiosa, the causal organism of Pierce's disease, was very sensitive to leaf extracts from ESF12 transformed plants. Further work is needed to verify the agricultural utility of ESF12 transformants. In New York, some transformants were resistant to powdery mildew and Botrytis fruit rot. Major conclusions, solutions, achievements and implications The following scientific achievements resulted from this cooperative BARD project: 1. Development and improvement of embryogenesis and tissue culture manipulation in grape, while extending these procedures to several agriculturally important cultivars both in Israel and USA. 2. Development and improvement of novel transformation procedures while developing transformation techniques for grape and other recalcitrant species. 3. Production of transgenic grapevines, characterization of transformed vines while studying the expression patterns of a marker gene under the control of different promoter as the 35S CaMV in different part of the plants including flowers and fruits. 4. Expression of anti-fungal genes in grape: establishment of transgenic plants and evaluation of gene expression. Development of techniques to insert multiple genes. 5. Isolation of novel grape specific promoter to control the expression of future antimicrobial genes. It is of great importance to report that significant progress was made in not only the development of transgenic grapevines, but also in the evaluation of their potential for increased resistance to disease as compared with the non engineered cultivar. In several cases, increased disease resistance was observed. More research and development is still needed before a product can be commercialized, yet our project lays a framework for further investigations.

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Coelho, Danilo Santa Cruz. Nota Técnica n. 65 (Diest) : Inteligência artificial em justiça e segurança pública : exemplos e recomendações para políticas públicas. Instituto de Pesquisa Econômica Aplicada (Ipea), 2024. https://doi.org/10.38116/ntdiest65-port.

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Nesta nota, apresentamos quatro exemplos de pesquisas do Instituto de Pesquisa Econômica Aplicada (Ipea) nas áreas de justiça e segurança pública que utilizam algoritmos de IA: três no subcampo de Aprendizado de Máquina (Machine Learning) e um, ainda em andamento, no subcampo de Processamento de Linguagem Natural. Também destacamos duas experiências práticas nessas áreas nos EUA que evidenciam os graves danos que a aplicação irrefletida dessas ferramentas pode causar, especialmente a grupos minoritários e vulneráveis, servindo como alerta para que a sociedade evite replicar essas práticas sem as devidas precauções. Por fim, com essa preocupação, listamos recomendações de especialistas renomados para a elaboração e uso dessas ferramentas em políticas públicas.

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Mbaye, Safiétou, Rémi Kouabenan, and Philippe Sarnin. L'explication naïve et la perception des risques comme des voies pour améliorer les pratiques de REX: des études dans l'industrie chimique et l'industrie nucléaire. Fondation pour une culture de sécurité industrielle, 2009. http://dx.doi.org/10.57071/311rex.

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L’analyse de l’accident soulève de nombreux enjeux dont le poids préfigure également des biais possibles dans l’explication causale et laisse apparaître que l’explication de l’accident peut difficilement être neutre. En l’occurrence, les difficultés rencontrées dans la conduite du REX relèvent en grande partie de conflits qui surviennent lors des analyses d’accidents. Ce document présente quatre études de terrain qui visaient à comprendre l’origine des conflits entre les acteurs du REX lors des analyses d’accidents et à mieux appréhender le rôle de la perception des risques et du climat de sécurité dans la motivation des acteurs à s’impliquer davantage dans les pratiques de REX. Les études sont conduites dans l’industrie chimique et l’industrie nucléaire et portent sur plus de 1000 agents de tous niveaux hiérarchiques (cadres, agents de maîtrise, techniciens, ouvriers) et tous domaines d’activité (prévention, production, maintenance, qualité). La méthodologie repose sur des entretiens, des observations de comité REX, une expérimentation et l’administration de deux questionnaires. Il en ressort que les démarches de REX sont davantage subies qu’elles ne sont portées par les responsables du traitement des accidents en raison des coûts qu’elles engendrent en temps et en énergie, mais aussi à cause du manque d’opérationnalité des règles de traitement des accidents. Il s’avère ensuite que l’absence de dialogue autour des causes des accidents entretient des doutes sur la crédibilité du REX auprès des opérateurs. Mais avant tout, il est clairement établi que la peur d’endosser la responsabilité de l’accident détermine fortement l’explication de l’accident qui devient notamment très défensive. Par exemple, les cadres et les ouvriers se renvoient systématiquement la causalité des accidents: plus ils se sentent menacés par l’analyse d’accident, plus ils en attribuent la survenue à des facteurs internes à l’autre groupe hiérarchique. Les études révèlent également comment les croyances sur les risques conduisent les individus à être plus attentifs au REX sur les accidents directement liés au cœur de métier de leur industrie et moins attentifs au REX sur les accidents de la vie courante, pourtant plus nombreux et plus graves. Enfin, nous montrons en quoi un bon climat de sécurité peut favoriser l’implication des acteurs dans les pratiques de REX.

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Homan, H. Jeffrey, Ron J. Johnson, James R. Thiele, and George M. Linz. European Starlings. U.S. Department of Agriculture, Animal and Plant Health Inspection Service, 2017. http://dx.doi.org/10.32747/2017.7207737.ws.

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European starlings (Sturnus vulgaris, Figure 1)are an invasive species in the United States. The first recorded release of the birds was in 1890 in New York City’s Central Park. Because starlings easily adapt to a variety of habitats, nest sites and food sources, the birds spread quickly across the country. Today, there are about 150 million starlings in North America. Conflicts between people and starlings occur mostly in agricultural settings. Starlings damage apples, blueberries, cherries, figs, grapes, peaches, and strawberries. Starlings gather at concentrated animal feeding operations (CAFOs) during late fall and winter. Starlings also cause human health problem, airplane hazards, and nuisance problems. European starlings are not protected by the Migratory Bird Treaty Act (MBTA).

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