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Journal articles on the topic 'Causal graphs'

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Author: Grafiati

Published: 9 March 2023

Last updated: 10 March 2023

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1

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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2

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 medical literature; yet, the result is typically a large graph of low precision. There are statistical methods for constructing causal graphs from observational data, but they are less suitable for dealing with a large number of covariates, which is the case in EMR data. Consequently, confounding variables are often identified by medical domain experts via a manual, expensive, and time-consuming process. We present a novel approach for automatically constructing causal graphs between medical conditions. The first part is a novel graph-based method to better capture causal relationships implied by medical literature, especially in the presence of multiple causal factors. Yet even after using these advanced text-analysis methods, the text data still contains many weak or uncertain causal connections. Therefore, we construct a second graph for these terms based on an EMR repository of over 1.5M patients. We combine the two graphs, leaving only edges that have both medical-text-based and observational evidence. We examine several strategies to carry out our approach, and compare the precision of the resulting graphs using medical experts. Our results show a significant improvement in the precision of any of our methods compared to the state of the art.

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3

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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4

Lipsky, Ari M., and Sander Greenland. "Causal Directed Acyclic Graphs." JAMA 327, no. 11 (March 15, 2022): 1083. http://dx.doi.org/10.1001/jama.2022.1816.

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5

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

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6

Peters, Jonas, and Peter Bühlmann. "Structural Intervention Distance for Evaluating Causal Graphs." Neural Computation 27, no. 3 (March 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 to compare CPDAGs, completed partially DAGs that represent Markov equivalence classes. The SID differs significantly from the widely used structural Hamming distance and therefore constitutes a valuable additional measure. We discuss properties of this distance and provide a (reasonably) efficient implementation with software code available on the first author’s home page.

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7

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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8

Mian, Osman A., Alexander Marx, and Jilles Vreeken. "Discovering Fully Oriented Causal Networks." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 10 (May 18, 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 the Minimum Description Length principle, which allows us to define a consistent and computable score based on non-parametric multivariate regression. To efficiently discover causal networks in practice, we introduce the GLOBE algorithm, which greedily adds, removes, and orients edges such that it minimizes the overall cost. Through an extensive set of experiments, we show GLOBE performs very well in practice, beating the state of the art by a margin.

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9

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 was, however, conditioned by an assumption from parameterised complexity theory that has no known useful relationship with the standard complexity classes. We approach the same problem from the perspective of standard complexity classes, and prove that planning is NP-hard for classes with unbounded components under an additional restriction we refer to as SP-closed. We then argue that most NP-hardness theorems for causal graphs are difficult to apply and, thus, prove a more general result; even if the component sizes grow slowly and the class is not densely populated with graphs, planning still cannot be tractable unless the polynomial hierachy collapses. Both these results still hold when restricted to the class of acyclic causal graphs. We finally give a partial characterization of the borderline between NP-hard and NP-intermediate classes, giving further insight into the problem.

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10

Habel, Christopher, and Cengiz Acarturk'. "Causal inference in graph-text constellations: Designing verbally annotated graphs." Tsinghua Science and Technology 16, no. 1 (February 2011): 7–12. http://dx.doi.org/10.1016/s1007-0214(11)70002-5.

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11

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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12

Levine, Eli, and J. Butler. "Causal Graphs and Concept-Mapping Assumptions." Applied System Innovation 1, no. 3 (July 24, 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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14

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

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15

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

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16

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

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17

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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18

Talvitie, Topi, and Mikko Koivisto. "Counting and Sampling Markov Equivalent Directed Acyclic Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 7984–91. http://dx.doi.org/10.1609/aaai.v33i01.33017984.

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Exploring directed acyclic graphs (DAGs) in a Markov equivalence class is pivotal to infer causal effects or to discover the causal DAG via appropriate interventional data. We consider counting and uniform sampling of DAGs that are Markov equivalent to a given DAG. These problems efficiently reduce to counting the moral acyclic orientations of a given undirected connected chordal graph on n vertices, for which we give two algorithms. Our first algorithm requires O(2nn4) arithmetic operations, improving a previous superexponential upper bound. The second requires O(k!2kk2n) operations, where k is the size of the largest clique in the graph; for bounded-degree graphs this bound is linear in n. After a single run, both algorithms enable uniform sampling from the equivalence class at a computational cost linear in the graph size. Empirical results indicate that our algorithms are superior to previously presented algorithms over a range of inputs; graphs with hundreds of vertices and thousands of edges are processed in a second on a desktop computer.

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19

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 information retrieval and natural language processing methods. Events are represented as event-phrase embeddings, which make it possible to group similar events into semantically cohesive clusters. A time series of the selected variables is given as input to a causal structure learning techniques to learn a causal graph associated with the topic that is being examined. The complete framework is applied to the New York Times dataset, which covers news for a period of 246 months (roughly 20 years), and is illustrated through a case study. An initial evaluation based on synthetic data is carried out to gain insight into the most effective time-series causality learning techniques. This evaluation comprises a systematic analysis of nine state-of-the-art causal structure learning techniques and two novel ensemble methods derived from the most effective techniques. Subsequently, the complete framework based on the most promising causal structure learning technique is evaluated with domain experts in a real-world scenario through the use of the presented case study. The proposed analysis offers valuable insights into the problems of identifying topic-relevant variables from large volumes of news and learning causal graphs from time series.

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20

Wieczorek, Aleksander, and Volker Roth. "Information Theoretic Causal Effect Quantification." Entropy 21, no. 10 (October 5, 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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21

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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22

Lu, Jing Xian, Christophe Dousson, and Francine Krief. "Self-Diagnosis Architecture for QoS and QoE Management in IPTV Services over IMS Network." Applied Mechanics and Materials 610 (August 2014): 568–78. http://dx.doi.org/10.4028/www.scientific.net/amm.610.568.

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We propose an autonomic QoS and QoE management architecture based on causal graphs for IPTV services over IP access networks. Our solution achieves a global self-diagnosis process based on local knowledge of each network element using a model-based diagnosis approach. We illustrate this novel approach in an IMS platform and detail the corresponding causal graph and diagnosis process for IPTV services.

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23

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 (January 2010): 111–27. http://dx.doi.org/10.1111/j.1467-9868.2009.00728.x.

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24

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 (July 18, 2020): 1659–76. http://dx.doi.org/10.1111/rssa.12594.

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25

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

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26

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 (July 12, 2019): 1–14. http://dx.doi.org/10.1145/3306346.3322977.

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27

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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28

Steiner, Peter M., Yongnam Kim, Courtney E. Hall, and Dan Su. "Graphical Models for Quasi-experimental Designs." Sociological Methods & Research 46, no. 2 (January 1, 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 to RD, IV, or PS designs reveals that the assumptions become stronger as the researcher’s control over treatment selection diminishes. We introduce limiting graphs for the RD design and conditional graphs for the latent subgroups of compliers, always takers, and never takers of the IV design, and argue that the PS is a collider that offsets confounding bias via collider bias.

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29

Fahrenthold, E. P., and J. D. Wargo. "Lagrangian Bond Graphs for Solid Continuum Dynamics Modeling." Journal of Dynamic Systems, Measurement, and Control 116, no. 2 (June 1, 1994): 178–92. http://dx.doi.org/10.1115/1.2899209.

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The limitations of existing continuum bond graph modeling techniques have effectively precluded their use in large order problems, where nonrepetitive graph structures and causal patterns are normally present. As a result, despite extensive publication of bond graph models for continuous systems simulations, bond graph methods have not offered a viable alternative to finite element analysis for the vast majority of practical problems. However, a new modeling approach combining Lagrangian (mass fixed) bond graphs with a selected finite element discretization scheme allows for direct simulation of a wide range of large order solid continuum dynamics problems. With appropriate modifications, including the use of Eulerian (space fixed) bond graphs, the method may be extended to include fluid dynamics modeling.

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30

Backstrom, Christer, Peter Jonsson, and Sebastian Ordyniak. "A Refined Understanding of Cost-Optimal Planning with Polytree Causal Graphs." Proceedings of the International Symposium on Combinatorial Search 9, no. 1 (September 1, 2021): 19–27. http://dx.doi.org/10.1609/socs.v9i1.18455.

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Complexity analysis based on the causal graphs of planning instances has emerged as a highly important area of research. In particular, tractability results have led to new methods for the identification of domain-independent heuristics. Important early examples of such tractability results have been presented by, for instance, Brafman & Domshlak and Katz & Keyder. More general results based on polytrees and bounding certain parameters were subsequently derived by Aghighi et al. and Ståhlberg. We continue this line of research by analyzing cost-optimal planning restricted to instances with a polytree causal graph, bounded domain size and bounded depth (i.e. the length of the longest directed path in the causal graph). We show that no further restrictions are necessary for tractability, thus generalizing the previous results. Our approach is based on a novel method of closely analysing optimal plans: we recursively decompose the causal graph in a way that allows for bounding the number of variable changes as a function of the depth, using a reording argument and a comparison with prefix trees of known size. We can then transform the planning instances into constraint satisfaction instances; an idea that has previously been exploited by, for example, Brafman & Domshlak and Bäckström. This allows us to utilise efficient algorithms for constraint optimisation over tree-structured instances.

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31

Yang, Hye-Won, and Sun-Geun Baek. "Causal Path Analysis of Teaching Competency based on the Structural Causal Model: Focusing on the Comparison across South Korea, England, and Finland using TALIS 2018 data." Korean Society for Educational Evaluation 35, no. 4 (December 31, 2022): 657–86. http://dx.doi.org/10.31158/jeev.2022.35.4.657.

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Based on the structural causal model, this study derived a causal graph that shows the causal relationship between the factors predicting the teaching competency of lower secondary school teachers in South Korea, the UK(England), and Finland. Also, it compared and analyzed the causal path to each country’s teaching competency. To this end, the data of lower secondary school teachers and principals, who participated in TALIS 2018, in Korea, the UK(England), and Finland were analyzed. First, the top 20 factors that predict teaching competency by each country were extracted by applying the mixed-effect random forest technique in consideration of the multi-layer structure of the data. Then, the causal graphs were derived by applying the causal discovery algorithm based on a structural causal model with the extracted predictors. As a result, there were common factors and discrimination factors in the top 20 predictors extracted from each national data, and the causal paths to teaching competency were compared and analyzed in the context of each country based on the causal graph by country. In addition, in the field of education, the possibility of using causal inference based on structural causal models was discussed, and the limitations and implications of this study were presented.

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32

Hines, Oliver, Karla Diaz-Ordaz, Stijn Vansteelandt, and Yalda Jamshidi. "Causal graphs for the analysis of genetic cohort data." Physiological Genomics 52, no. 9 (September 1, 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 communicate, the causal assumptions upon which they are based, so that methods are transparent, and findings are clinically relevant. Causal graphs can be used to represent causal assumptions graphically and provide insights into the limitations associated with different analysis methods. Here we review GWAS and MR from a causal perspective, to build up intuition for causal diagrams in genetic problems. We also examine issues of confounding by ancestry and comment on approaches for dealing with such confounding, as well as discussing approaches for dealing with selection biases arising from study design.

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33

Filk, Thomas. "Proper time and Minkowski structure on causal graphs." Classical and Quantum Gravity 18, no. 14 (June 29, 2001): 2785–95. http://dx.doi.org/10.1088/0264-9381/18/14/311.

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

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35

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

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36

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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37

Ghassami, AmirEmad, Saber Salehkaleybar, Negar Kiyavash, and Kun Zhang. "Counting and Sampling from Markov Equivalent DAGs Using Clique Trees." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 3664–71. http://dx.doi.org/10.1609/aaai.v33i01.33013664.

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A directed acyclic graph (DAG) is the most common graphical model for representing causal relationships among a set of variables. When restricted to using only observational data, the structure of the ground truth DAG is identifiable only up to Markov equivalence, based on conditional independence relations among the variables. Therefore, the number of DAGs equivalent to the ground truth DAG is an indicator of the causal complexity of the underlying structure–roughly speaking, it shows how many interventions or how much additional information is further needed to recover the underlying DAG. In this paper, we propose a new technique for counting the number of DAGs in a Markov equivalence class. Our approach is based on the clique tree representation of chordal graphs. We show that in the case of bounded degree graphs, the proposed algorithm is polynomial time. We further demonstrate that this technique can be utilized for uniform sampling from a Markov equivalence class, which provides a stochastic way to enumerate DAGs in the equivalence class and may be needed for finding the best DAG or for causal inference given the equivalence class as input. We also extend our counting and sampling method to the case where prior knowledge about the underlying DAG is available, and present applications of this extension in causal experiment design and estimating the causal effect of joint interventions.

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Makarov, Ilya, Andrey Savchenko, Arseny Korovko, Leonid Sherstyuk, Nikita Severin, Dmitrii Kiselev, Aleksandr Mikheev, and Dmitrii Babaev. "Temporal network embedding framework with causal anonymous walks representations." PeerJ Computer Science 8 (January 20, 2022): e858. http://dx.doi.org/10.7717/peerj-cs.858.

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Many tasks in graph machine learning, such as link prediction and node classification, are typically solved using representation learning. Each node or edge in the network is encoded via an embedding. Though there exists a lot of network embeddings for static graphs, the task becomes much more complicated when the dynamic (i.e., temporal) network is analyzed. In this paper, we propose a novel approach for dynamic network representation learning based on Temporal Graph Network by using a highly custom message generating function by extracting Causal Anonymous Walks. We provide a benchmark pipeline for the evaluation of temporal network embeddings. This work provides the first comprehensive comparison framework for temporal network representation learning for graph machine learning problems involving node classification and link prediction in every available setting. The proposed model outperforms state-of-the-art baseline models. The work also justifies their difference based on evaluation in various transductive/inductive edge/node classification tasks. In addition, we show the applicability and superior performance of our model in the real-world downstream graph machine learning task provided by one of the top European banks, involving credit scoring based on transaction data.

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39

Pearl, Judea. "Causation and decision: On Dawid’s “Decision theoretic foundation of statistical causality”." Journal of Causal Inference 10, no. 1 (January 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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40

Brafman, R. I., and C. Domshlak. "Structure and Complexity in Planning with Unary Operators." Journal of Artificial Intelligence Research 18 (April 1, 2003): 315–49. http://dx.doi.org/10.1613/jair.1146.

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Unary operator domains -- i.e., domains in which operators have a single effect -- arise naturally in many control problems. In its most general form, the problem of STRIPS planning in unary operator domains is known to be as hard as the general STRIPS planning problem -- both are PSPACE-complete. However, unary operator domains induce a natural structure, called the domain's causal graph. This graph relates between the preconditions and effect of each domain operator. Causal graphs were exploited by Williams and Nayak in order to analyze plan generation for one of the controllers in NASA's Deep-Space One spacecraft. There, they utilized the fact that when this graph is acyclic, a serialization ordering over any subgoal can be obtained quickly. In this paper we conduct a comprehensive study of the relationship between the structure of a domain's causal graph and the complexity of planning in this domain. On the positive side, we show that a non-trivial polynomial time plan generation algorithm exists for domains whose causal graph induces a polytree with a constant bound on its node indegree. On the negative side, we show that even plan existence is hard when the graph is a directed-path singly connected DAG. More generally, we show that the number of paths in the causal graph is closely related to the complexity of planning in the associated domain. Finally we relate our results to the question of complexity of planning with serializable subgoals.

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CABALAR, PEDRO, and JORGE FANDINNO. "Enablers and inhibitors in causal justifications of logic programs." Theory and Practice of Logic Programming 17, no. 1 (May 3, 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 different nature from regular cause-effect relations. The most important result is a formal comparison to the recent algebraic approaches for justifications in logic programming:Why-not ProvenanceandCausal Graphs. We show that the current approach extends both Why-not Provenance and Causal Graphs justifications under the well-founded semantics and, as a byproduct, we also establish a formal relation between these two approaches.

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Gaskell, Amy L., and Jamie W. Sleigh. "An Introduction to Causal Diagrams for Anesthesiology Research." Anesthesiology 132, no. 5 (May 1, 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 whether anesthetic technique (total intravenous anesthesia vs. volatile) affects outcome after cancer surgery as a basis for a series of example directed acyclic graphs, which demonstrate how variables can be chosen to statistically control confounding and other sources of bias. We also illustrate how controlling for the wrong variables can introduce, rather than eliminate, bias; and how directed acyclic graphs can help us diagnose this problem. This is a rapidly evolving field, and we cover only the most basic elements. The true promise of these techniques is that it may become possible to make robust statements about causation from observational studies—without the expense and artificiality of randomized controlled trials.

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CABALAR, PEDRO, JORGE FANDINNO, and MICHAEL FINK. "Causal Graph Justifications of Logic Programs." Theory and Practice of Logic Programming 14, no. 4-5 (July 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 obtained in a purely semantic way, by algebraic operations (product, sum and application) on a lattice of causal values whose ordering relation expresses when a justification is stronger than another. Finally, for programs with negation, we define the concept of causal stable model by introducing an analogous transformation to Gelfond and Lifschitz's program reduct. As a result, default negation behaves as “absence of proof” and no justification is derived from negative literals, something that turns out convenient for elaboration tolerance, as we explain with a running example.

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Hoffmann, Joerg. "Where Ignoring Delete Lists Works, Part II: Causal Graphs." Proceedings of the International Conference on Automated Planning and Scheduling 21 (March 22, 2011): 98–105. http://dx.doi.org/10.1609/icaps.v21i1.13469.

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The ignoring delete lists relaxation is of paramount importance for both satisficing and optimal planning. In earlier work (Hoffmann 2005), it was observed that the optimal relaxation heuristic h+ has amazing qualities in many classical planning benchmarks, in particular pertaining to the complete absence of local minima. The proofs of this are hand-made, raising the question whether such proofs can be lead automatically by domain analysis techniques. In contrast to earlier disappointing results (Hoffmann 2005) — the analysis method has exponential runtime and succeeds only in two extremely simple benchmark domains — we herein answer this question in the affirmative. We establish connections between causal graph structure and h+ topology. This results in low-order polynomial time analysis methods, implemented in a tool we call TorchLight. Of the 12 domains where the absence of local minima has been proved, TorchLight gives strong success guarantees in 8 domains. Empirically, its analysis exhibits strong performance in a further 2 of these domains, plus in 4 more domains where local minima may exist but are rare. In this way, TorchLight can distinguish ``easy'' domains from "hard" ones. By summarizing structural reasons for analysis failure, TorchLight also provides diagnostic output indicating domain aspects that may cause local minima.

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Spirtes, Peter, and Clark Glymour. "An Algorithm for Fast Recovery of Sparse Causal Graphs." Social Science Computer Review 9, no. 1 (April 1991): 62–72. http://dx.doi.org/10.1177/089443939100900106.

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Mei, Jonathan, and Jose M. F. Moura. "Signal Processing on Graphs: Causal Modeling of Unstructured Data." IEEE Transactions on Signal Processing 65, no. 8 (April 15, 2017): 2077–92. http://dx.doi.org/10.1109/tsp.2016.2634543.

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Ligęza, Antoni. "Functional Causal Graphs. Yet Another Model for Diagnostic Reasoning." IFAC Proceedings Volumes 30, no. 18 (August 1997): 1101–6. http://dx.doi.org/10.1016/s1474-6670(17)42543-2.

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Miles, Simon, Paul Groth, Steve Munroe, Sheng Jiang, Thibaut Assandri, and Luc Moreau. "Extracting causal graphs from an open provenance data model." Concurrency and Computation: Practice and Experience 20, no. 5 (2008): 577–86. http://dx.doi.org/10.1002/cpe.1236.

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Yu, Xuewen, and Jim Q. Smith. "Causal Algebras on Chain Event Graphs with Informed Missingness for System Failure." Entropy 23, no. 10 (October 6, 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 demonstrated that an event tree rather than a BN could provide an alternative framework that could capture most of the causal concepts needed within this domain. In particular, a causal calculus for a specific type of intervention, called a remedial intervention, was devised on this tree-like graph. In this paper, we extend the use of this framework to show that not only remedial maintenance interventions but also interventions associated with routine maintenance can be well-defined using this alternative class of graphical model. We also show that the complexity in making inference about the potential relationships between causes and failures in a missing data situation in the domain of system reliability can be elegantly addressed using this new methodology. Causal modelling using a CEG is illustrated through examples drawn from the study of reliability of an energy distribution network.

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Sun, Wenbo. "Sampling and Counting Acyclic Orientations in Chordal Graphs (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (June 28, 2022): 13061–62. http://dx.doi.org/10.1609/aaai.v36i11.21667.

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Sampling of chordal graphs and various types of acyclic orientations over chordal graphs plays a central role in several AI applications such as causal structure learning. For a given undirected graph, an acyclic orientation is an assignment of directions to all of its edges which makes the resulting directed graph cycle-free. Sampling is often closely related to the corresponding counting problem. Counting of acyclic orientations of a given chordal graph can be done in polynomial time, but the previously known techniques do not seem to lead to a corresponding (efficient) sampler. In this work, we propose a dynamic programming framework which yields a counter and a uniform sampler, both of which run in (essentially) linear time. An interesting feature of our sampler is that it is a stand-alone algorithm that, unlike other DP-based samplers, does not need any preprocessing which determines the corresponding counts.

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