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Journal articles on the topic 'Causal graphs'

Author: Grafiati

Published: 9 March 2023

Last updated: 31 July 2025

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1

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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2

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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3

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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4

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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5

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 bi

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6

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,

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7

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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8

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 in

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9

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 n

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10

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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11

Chen, Hubie, and Omer Giménez. "Causal graphs and structurally restricted planning." Journal of Computer and System Sciences 76, no. 7 (2010): 579–92. http://dx.doi.org/10.1016/j.jcss.2009.10.013.

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12

Montmain, Jacky, and Lydie Leyval. "Causal Graphs for Model Based Diagnosis." IFAC Proceedings Volumes 27, no. 5 (1994): 329–37. http://dx.doi.org/10.1016/s1474-6670(17)48049-9.

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13

Thwaites, Peter, Jim Q. Smith, and Eva Riccomagno. "Causal analysis with Chain Event Graphs." Artificial Intelligence 174, no. 12-13 (2010): 889–909. http://dx.doi.org/10.1016/j.artint.2010.05.004.

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14

Thwaites, Peter. "Causal identifiability via Chain Event Graphs." Artificial Intelligence 195 (February 2013): 291–315. http://dx.doi.org/10.1016/j.artint.2012.09.003.

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15

Chen, Wenyu, Mathias Drton, and Ali Shojaie. "Causal Structural Learning via Local Graphs." SIAM Journal on Mathematics of Data Science 5, no. 2 (2023): 280–305. http://dx.doi.org/10.1137/20m1362796.

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16

Steiner, Peter M., Yongnam Kim, Courtney E. Hall, and Dan Su. "Graphical Models for Quasi-experimental Designs." Sociological Methods & Research 46, no. 2 (2015): 155–88. http://dx.doi.org/10.1177/0049124115582272.

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Randomized controlled trials (RCTs) and quasi-experimental designs like regression discontinuity (RD) designs, instrumental variable (IV) designs, and matching and propensity score (PS) designs are frequently used for inferring causal effects. It is well known that the features of these designs facilitate the identification of a causal estimand and, thus, warrant a causal interpretation of the estimated effect. In this article, we discuss and compare the identifying assumptions of quasi-experiments using causal graphs. The increasing complexity of the causal graphs as one switches from an RCT

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17

Ferreira, Simon, and Charles K. Assaad. "Identifiability of Direct Effects from Summary Causal Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 18 (2024): 20387–94. http://dx.doi.org/10.1609/aaai.v38i18.30021.

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Dynamic structural causal models (SCMs) are a powerful framework for reasoning in dynamic systems about direct effects which measure how a change in one variable affects another variable while holding all other variables constant. The causal relations in a dynamic structural causal model can be qualitatively represented with an acyclic full-time causal graph. Assuming linearity and no hidden confounding and given the full-time causal graph, the direct causal effect is always identifiable. However, in many application such a graph is not available for various reasons but nevertheless experts ha

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18

Nordon, Galia, Gideon Koren, Varda Shalev, Benny Kimelfeld, Uri Shalit, and Kira Radinsky. "Building Causal Graphs from Medical Literature and Electronic Medical Records." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 1102–9. http://dx.doi.org/10.1609/aaai.v33i01.33011102.

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Large repositories of medical data, such as Electronic Medical Record (EMR) data, are recognized as promising sources for knowledge discovery. Effective analysis of such repositories often necessitate a thorough understanding of dependencies in the data. For example, if the patient age is ignored, then one might wrongly conclude a causal relationship between cataract and hypertension. Such confounding variables are often identified by causal graphs, where variables are connected by causal relationships. Current approaches to automatically building such graphs are based on text analysis over me

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19

Ferreira, Simon, and Charles K. Assaad. "Identifying Macro Conditional Independencies and Macro Total Effects in Summary Causal Graphs with Latent Confounding." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 25 (2025): 26787–95. https://doi.org/10.1609/aaai.v39i25.34882.

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Understanding causal relations in dynamic systems is essential in epidemiology. While causal inference methods have been extensively studied, they often rely on fully specified causal graphs, which may not always be available in complex dynamic systems. Partially specified causal graphs, and in particular summary causal graphs (SCGs), provide a simplified representation of causal relations between time series when working spacio-temporal data, omitting temporal information and focusing on causal structures between clusters of of temporal variables. Unlike fully specified causal graphs, SCGs ca

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20

VanderWeele, Tyler J., and James M. Robins. "Signed directed acyclic graphs for causal inference." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 72, no. 1 (2010): 111–27. http://dx.doi.org/10.1111/j.1467-9868.2009.00728.x.

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21

Ogburn, Elizabeth L., Ilya Shpitser, and Youjin Lee. "Causal inference, social networks and chain graphs." Journal of the Royal Statistical Society: Series A (Statistics in Society) 183, no. 4 (2020): 1659–76. http://dx.doi.org/10.1111/rssa.12594.

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22

Kämpke, T. "Inferencing the graphs of causal Markov fields." Mathematical and Computer Modelling 25, no. 3 (1997): 1–22. http://dx.doi.org/10.1016/s0895-7177(97)00011-3.

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23

Roussel, Robin, Marie-Paule Cani, Jean-Claude Léon, and Niloy J. Mitra. "Designing chain reaction contraptions from causal graphs." ACM Transactions on Graphics 38, no. 4 (2019): 1–14. http://dx.doi.org/10.1145/3306346.3322977.

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24

Peters, Jonas, and Peter Bühlmann. "Structural Intervention Distance for Evaluating Causal Graphs." Neural Computation 27, no. 3 (2015): 771–99. http://dx.doi.org/10.1162/neco_a_00708.

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Causal inference relies on the structure of a graph, often a directed acyclic graph (DAG). Different graphs may result in different causal inference statements and different intervention distributions. To quantify such differences, we propose a (pre-)metric between DAGs, the structural intervention distance (SID). The SID is based on a graphical criterion only and quantifies the closeness between two DAGs in terms of their corresponding causal inference statements. It is therefore well suited for evaluating graphs that are used for computing interventions. Instead of DAGs, it is also possible

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25

Jonsson, Anders, Peter Jonsson, and Tomas Lööw. "Limitations of acyclic causal graphs for planning." Artificial Intelligence 210 (May 2014): 36–55. http://dx.doi.org/10.1016/j.artint.2014.02.002.

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26

Tan, Fiona Anting, Debdeep Paul, Sahim Yamaura, Miura Koji, and See-Kiong Ng. "Constructing and Interpreting Causal Knowledge Graphs from News." Proceedings of the AAAI Symposium Series 1, no. 1 (2023): 52–59. http://dx.doi.org/10.1609/aaaiss.v1i1.27476.

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Many financial jobs rely on news to learn about causal events in the past and present, to make informed decisions and predictions about the future. With the ever-increasing amount of news available online, there is a need to automate the extraction of causal events from unstructured texts. In this work, we propose a methodology to construct causal knowledge graphs (KGs) from news using two steps: (1) Extraction of Causal Relations, and (2) Argument Clustering and Representation into KG. We aim to build graphs that emphasize on recall, precision and interpretability. For extraction, although ma

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27

Pearl, Judea. "Causation and decision: On Dawid’s “Decision theoretic foundation of statistical causality”." Journal of Causal Inference 10, no. 1 (2022): 221–26. http://dx.doi.org/10.1515/jci-2022-0046.

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Abstract In a recent issue of this journal, Philip Dawid (2021) proposes a framework for causal inference that is based on statistical decision theory and that is, in many aspects, compatible with the familiar framework of causal graphs (e.g., Directed Acyclic Graphs (DAGs)). This editorial compares the methodological features of the two frameworks as well as their epistemological basis.

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28

Mian, Osman A., Alexander Marx, and Jilles Vreeken. "Discovering Fully Oriented Causal Networks." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 10 (2021): 8975–82. http://dx.doi.org/10.1609/aaai.v35i10.17085.

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We study the problem of inferring causal graphs from observational data. We are particularly interested in discovering graphs where all edges are oriented, as opposed to the partially directed graph that the state of the art discover. To this end, we base our approach on the algorithmic Markov condition. Unlike the statistical Markov condition, it uniquely identifies the true causal network as the one that provides the simplest— as measured in Kolmogorov complexity—factorization of the joint distribution. Although Kolmogorov complexity is not computable, we can approximate it from above via th

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29

Ji, Qirui, Jiangmeng Li, Jie Hu, Rui Wang, Changwen Zheng, and Fanjiang Xu. "Rethinking Dimensional Rationale in Graph Contrastive Learning from Causal Perspective." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 11 (2024): 12810–20. http://dx.doi.org/10.1609/aaai.v38i11.29177.

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Graph contrastive learning is a general learning paradigm excelling at capturing invariant information from diverse perturbations in graphs. Recent works focus on exploring the structural rationale from graphs, thereby increasing the discriminability of the invariant information. However, such methods may incur in the mis-learning of graph models towards the interpretability of graphs, and thus the learned noisy and task-agnostic information interferes with the prediction of graphs. To this end, with the purpose of exploring the intrinsic rationale of graphs, we accordingly propose to capture

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30

Hines, Oliver, Karla Diaz-Ordaz, Stijn Vansteelandt, and Yalda Jamshidi. "Causal graphs for the analysis of genetic cohort data." Physiological Genomics 52, no. 9 (2020): 369–78. http://dx.doi.org/10.1152/physiolgenomics.00115.2019.

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The increasing availability of genetic cohort data has led to many genome-wide association studies (GWAS) successfully identifying genetic associations with an ever-expanding list of phenotypic traits. Association, however, does not imply causation, and therefore methods have been developed to study the issue of causality. Under additional assumptions, Mendelian randomization (MR) studies have proved popular in identifying causal effects between two phenotypes, often using GWAS summary statistics. Given the widespread use of these methods, it is more important than ever to understand, and comm

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31

CABALAR, PEDRO, and JORGE FANDINNO. "Enablers and inhibitors in causal justifications of logic programs." Theory and Practice of Logic Programming 17, no. 1 (2016): 49–74. http://dx.doi.org/10.1017/s1471068416000107.

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AbstractIn this paper, we propose an extension of logic programming where each default literal derived from the well-founded model is associated to a justification represented as an algebraic expression. This expression contains both causal explanations (in the form of proof graphs built with rule labels) and terms under the scope of negation that stand for conditions that enable or disable the application of causal rules. Using some examples, we discuss how these new conditions, we respectively callenablersandinhibitors, are intimately related to default negation and have an essentially diffe

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32

Gao, Hang, Jiangmeng Li, Wenwen Qiang, et al. "Robust Causal Graph Representation Learning against Confounding Effects." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 6 (2023): 7624–32. http://dx.doi.org/10.1609/aaai.v37i6.25925.

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The prevailing graph neural network models have achieved significant progress in graph representation learning. However, in this paper, we uncover an ever-overlooked phenomenon: the pre-trained graph representation learning model tested with full graphs underperforms the model tested with well-pruned graphs. This observation reveals that there exist confounders in graphs, which may interfere with the model learning semantic information, and current graph representation learning methods have not eliminated their influence. To tackle this issue, we propose Robust Causal Graph Representation Lear

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33

Dong, Shuyu, Michele Sebag, Kento Uemura, et al. "DCILP: A Distributed Approach for Large-Scale Causal Structure Learning." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 15 (2025): 16345–53. https://doi.org/10.1609/aaai.v39i15.33795.

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Causal learning tackles the computationally demanding task of estimating causal graphs. This paper introduces a new divide-and-conquer approach for causal graph learning, called DCILP. In the divide phase, the Markov blanket MB(Xi) of each variable Xi is identified, and causal learning subproblems associated with each MB(Xi) are independently addressed in parallel. This approach benefits from a more favorable ratio between the number of data samples and the number of variables considered. In counterpart, it can be adversely affected by the presence of hidden confounders, as variables external

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34

Liu, Haitian, and Rong Jiang. "A Causal Graph-Based Approach for APT Predictive Analytics." Electronics 12, no. 8 (2023): 1849. http://dx.doi.org/10.3390/electronics12081849.

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In recent years, complex multi-stage cyberattacks have become more common, for which audit log data are a good source of information for online monitoring. However, predicting cyber threat events based on audit logs remains an open research problem. This paper explores advanced persistent threat (APT) audit log information and uses a combination of causal graphs and deep learning techniques to perform predictive analysis of APT. The study focuses on two different methods of constructing malicious activity scenarios, including those based on malicious entity evolving graphs and malicious entity

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35

Filk, Thomas. "Proper time and Minkowski structure on causal graphs." Classical and Quantum Gravity 18, no. 14 (2001): 2785–95. http://dx.doi.org/10.1088/0264-9381/18/14/311.

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36

Bae, Juhee, Tove Helldin, and Maria Riveiro. "Understanding Indirect Causal Relationships in Node‐Link Graphs." Computer Graphics Forum 36, no. 3 (2017): 411–21. http://dx.doi.org/10.1111/cgf.13198.

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37

Gawthrop, P. J., and L. Smith. "Causal augmentation of bond graphs with algebraic loops." Journal of the Franklin Institute 329, no. 2 (1992): 291–303. http://dx.doi.org/10.1016/0016-0032(92)90035-f.

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38

Amblard, Pierre-Olivier, and Olivier Michel. "Causal conditioning and instantaneous coupling in causality graphs." Information Sciences 264 (April 2014): 279–90. http://dx.doi.org/10.1016/j.ins.2013.12.037.

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39

Bäckström, C., and P. Jonsson. "A Refined View of Causal Graphs and Component Sizes: SP-Closed Graph Classes and Beyond." Journal of Artificial Intelligence Research 47 (July 30, 2013): 575–611. http://dx.doi.org/10.1613/jair.3968.

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The causal graph of a planning instance is an important tool for planning both in practice and in theory. The theoretical studies of causal graphs have largely analysed the computational complexity of planning for instances where the causal graph has a certain structure, often in combination with other parameters like the domain size of the variables. Chen and Giménez ignored even the structure and considered only the size of the weakly connected components. They proved that planning is tractable if the components are bounded by a constant and otherwise intractable. Their intractability result

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40

Gaskell, Amy L., and Jamie W. Sleigh. "An Introduction to Causal Diagrams for Anesthesiology Research." Anesthesiology 132, no. 5 (2020): 951–67. http://dx.doi.org/10.1097/aln.0000000000003193.

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Abstract Making good decisions in the era of Big Data requires a sophisticated approach to causality. We are acutely aware that association ≠ causation, yet untangling the two remains one of our greatest challenges. This realization has stimulated a Causal Revolution in epidemiology, and the lessons learned are highly relevant to anesthesia research. This article introduces readers to directed acyclic graphs; a cornerstone of modern causal inference techniques. These diagrams provide a robust framework to address sources of bias and discover causal effects. We use the topical question of wheth

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41

Xu, Yiming, Bin Shi, Zhen Peng, Huixiang Liu, Bo Dong, and Chen Chen. "Out-of-Distribution Generalization on Graphs via Progressive Inference." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 12 (2025): 12963–71. https://doi.org/10.1609/aaai.v39i12.33414.

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The development and evaluation of graph neural networks (GNNs) generally follow the independent and identically distributed (i.i.d.) assumption. Yet this assumption is often untenable in practice due to the uncontrollable data generation mechanism. In particular, when the data distribution shows a significant shift, most GNNs would fail to produce reliable predictions and may even make decisions randomly. One of the most promising solutions to improve the model generalization is to pick out causal invariant parts in the input graph. Nonetheless, we observe a significant distribution gap betwee

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42

CABALAR, PEDRO, JORGE FANDINNO, and MICHAEL FINK. "Causal Graph Justifications of Logic Programs." Theory and Practice of Logic Programming 14, no. 4-5 (2014): 603–18. http://dx.doi.org/10.1017/s1471068414000234.

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AbstractIn this work we propose a multi-valued extension of logic programs under the stable models semantics where each true atom in a model is associated with a set of justifications. These justifications are expressed in terms of causal graphs formed by rule labels and edges that represent their application ordering. For positive programs, we show that the causal justifications obtained for a given atom have a direct correspondence to (relevant) syntactic proofs of that atom using the program rules involved in the graphs. The most interesting contribution is that this causal information is o

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43

Jeong, Hyunchai, Adiba Ejaz, Jin Tian, and Elias Bareinboim. "Testing Causal Models with Hidden Variables in Polynomial Delay via Conditional Independencies." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 25 (2025): 26813–22. https://doi.org/10.1609/aaai.v39i25.34885.

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Testing a hypothesized causal model against observational data is a key prerequisite for many causal inference tasks. A natural approach is to test whether the conditional independence relations (CIs) assumed in the model hold in the data. While a model can assume exponentially many CIs (with respect to the number of variables), testing all of them is both impractical and unnecessary. Causal graphs, which encode these CIs in polynomial space, give rise to local Markov properties that enable model testing with a significantly smaller subset of CIs. Model testing based on local properties requir

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44

Ma, Jing, Mengting Wan, Longqi Yang, Jundong Li, Brent Hecht, and Jaime Teevan. "Causal Effect Estimation under Interference on Hypergraphs." AI Matters 9, no. 2 (2023): 15–19. http://dx.doi.org/10.1145/3609468.3609472.

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Hypergraphs offer a powerful abstraction for representing multi-way group interactions, allowing hyperedges to connect any number of nodes. In contrast to prevailing approaches that focus on capturing statistical dependencies, our research explores hypergraphs from a causal perspective. Specifically, we tackle the problem of estimating individual treatment effects (ITE) on hypergraphs, aiming to determine the causal impact of interventions (e.g., wearing face covering) on outcomes (e.g., COVID-19 infection) for each individual node. Existing ITE estimation methods either assume no interference

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45

Yang, Dezhi, Xintong He, Jun Wang, Guoxian Yu, Carlotta Domeniconi, and Jinglin Zhang. "Federated Causality Learning with Explainable Adaptive Optimization." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 15 (2024): 16308–15. http://dx.doi.org/10.1609/aaai.v38i15.29566.

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Discovering the causality from observational data is a crucial task in various scientific domains. With increasing awareness of privacy, data are not allowed to be exposed, and it is very hard to learn causal graphs from dispersed data, since these data may have different distributions. In this paper, we propose a federated causal discovery strategy (FedCausal) to learn the unified global causal graph from decentralized heterogeneous data. We design a global optimization formula to naturally aggregate the causal graphs from client data and constrain the acyclicity of the global graph without e

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46

Primbs, Maximilian A., Gijsbert Bijlstra, Rob W. Holland, and Felix Thoemmes. "Causal Inference for Dummies: A Tutorial on Directed Acyclic Graphs and Balancing Weights." Social Cognition 43, no. 3 (2025): 217–37. https://doi.org/10.1521/soco.2025.43.3.217.

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Traditionally, causal claims in social cognition research have been reserved for experimental designs. However, restricting causal claims to experimental research limits the type of questions that can be answered satisfactorily—including questions about geographical differences or changes over time recently popularized in the field of social cognition. In this tutorial, we outline a principled approach to causal inference for nonexperimental designs. We describe how researchers can use directed acyclic graphs to make their causal model explicit and discuss one strategy to estimate causal effec

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47

Yu, Xuewen, and Jim Q. Smith. "Causal Algebras on Chain Event Graphs with Informed Missingness for System Failure." Entropy 23, no. 10 (2021): 1308. http://dx.doi.org/10.3390/e23101308.

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Graph-based causal inference has recently been successfully applied to explore system reliability and to predict failures in order to improve systems. One popular causal analysis following Pearl and Spirtes et al. to study causal relationships embedded in a system is to use a Bayesian network (BN). However, certain causal constructions that are particularly pertinent to the study of reliability are difficult to express fully through a BN. Our recent work demonstrated the flexibility of using a Chain Event Graph (CEG) instead to capture causal reasoning embedded within engineers’ reports. We de

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48

Assaad, Charles K., Emilie Devijver, and Eric Gaussier. "Entropy-Based Discovery of Summary Causal Graphs in Time Series." Entropy 24, no. 8 (2022): 1156. http://dx.doi.org/10.3390/e24081156.

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This study addresses the problem of learning a summary causal graph on time series with potentially different sampling rates. To do so, we first propose a new causal temporal mutual information measure for time series. We then show how this measure relates to an entropy reduction principle that can be seen as a special case of the probability raising principle. We finally combine these two ingredients in PC-like and FCI-like algorithms to construct the summary causal graph. There algorithm are evaluated on several datasets, which shows both their efficacy and efficiency.

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49

Maisonnave, Mariano, Fernando Delbianco, Fernando Tohme, Evangelos Milios, and Ana G. Maguitman. "Causal graph extraction from news: a comparative study of time-series causality learning techniques." PeerJ Computer Science 8 (August 3, 2022): e1066. http://dx.doi.org/10.7717/peerj-cs.1066.

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Causal graph extraction from news has the potential to aid in the understanding of complex scenarios. In particular, it can help explain and predict events, as well as conjecture about possible cause-effect connections. However, limited work has addressed the problem of large-scale extraction of causal graphs from news articles. This article presents a novel framework for extracting causal graphs from digital text media. The framework relies on topic-relevant variables representing terms and ongoing events that are selected from a domain under analysis by applying specially developed informati

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Kummerfeld, Erich. "A simple interpretation of undirected edges in essential graphs is wrong." PLOS ONE 16, no. 4 (2021): e0249415. http://dx.doi.org/10.1371/journal.pone.0249415.

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Abstract:

Artificial intelligence for causal discovery frequently uses Markov equivalence classes of directed acyclic graphs, graphically represented as essential graphs, as a way of representing uncertainty in causal directionality. There has been confusion regarding how to interpret undirected edges in essential graphs, however. In particular, experts and non-experts both have difficulty quantifying the likelihood of uncertain causal arrows being pointed in one direction or another. A simple interpretation of undirected edges treats them as having equal odds of being oriented in either direction, but

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