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Artigos de revistas sobre o tema "Causal Diagrams"

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Autor: Grafiati

Publicado: 7 de julho de 2024

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1

Castello, Jonathan, Patrick Redmond e Lindsey Kuper. "Inductive Diagrams for Causal Reasoning". Proceedings of the ACM on Programming Languages 8, OOPSLA1 (29 de abril de 2024): 529–54. http://dx.doi.org/10.1145/3649830.

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The Lamport diagram is a pervasive and intuitive tool for informal reasoning about “happens-before” relationships in a concurrent system. However, traditional axiomatic formalizations of Lamport diagrams can be painful to work with in a mechanized setting like Agda. We propose an alternative, inductive formalization — the causal separation diagram (CSD) — that takes inspiration from string diagrams and concurrent separation logic, but enjoys a graphical syntax similar to Lamport diagrams. Critically, CSDs are based on the idea that causal relationships between events are witnessed by the paths that information follows between them. To that end, we model “happens-before” as a dependent type of paths between events. The inductive formulation of CSDs enables their interpretation into a variety of semantic domains. We demonstrate the interpretability of CSDs with a case study on properties of logical clocks , widely-used mechanisms for reifying causal relationships as data. We carry out this study by implementing a series of interpreters for CSDs, culminating in a generic proof of Lamport’s clock condition that is parametric in a choice of clock. We instantiate this proof on Lamport’s scalar clock, on Mattern’s vector clock, and on the matrix clocks of Raynal et al. and of Wuu and Bernstein, yielding verified implementations of each. The CSD formalism and our case study are mechanized in the Agda proof assistant.

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2

Schisterman, Enrique F., Neil J. Perkins, Sunni L. Mumford, Katherine A. Ahrens e Emily M. Mitchell. "Collinearity and Causal Diagrams". Epidemiology 28, n.º 1 (janeiro de 2017): 47–53. http://dx.doi.org/10.1097/ede.0000000000000554.

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3

Ogburn, Elizabeth L., e Tyler J. VanderWeele. "Causal Diagrams for Interference". Statistical Science 29, n.º 4 (novembro de 2014): 559–78. http://dx.doi.org/10.1214/14-sts501.

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4

Yen, Chi-Hsien (Eric), Haocong Cheng, Grace Yu-Chun Yen, Brian P. Bailey e Yun Huang. "Narratives + Diagrams: An Integrated Approach for Externalizing and Sharing People's Causal Beliefs". Proceedings of the ACM on Human-Computer Interaction 5, CSCW2 (13 de outubro de 2021): 1–27. http://dx.doi.org/10.1145/3479588.

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Causal knowledge is of interest in many areas, such as statistics and machine learning, as it allows people and algorithms to predict outcomes and make data-driven decisions. Researchers in CSCW have proposed tools and workflows to externalize causal knowledge or beliefs from a group of people; however, most of the generated causal diagrams lack a deeper understanding of the causal mechanisms or could not capture diverse beliefs. By integrating narratives with causal diagrams, we implemented an interactive system that allows users to 1) write narratives to rationalize their perceived causal relationships, 2) visualize their causal models using directed diagrams, and 3) review and utilize others' causal diagrams and narratives. We conducted a user study (N=20) to learn how participants leveraged this integrated approach to externalize their perceived causal models for a given application context. Our results showed that the approach implemented in our tool enabled the externalization of users' causal beliefs (e.g., how and why a causal relationship might occur), allowed blind spots of individuals' causal reasoning to be revealed (e.g., learning new ideas from peers), and inspired their causal reasoning (e.g., revising or adding new causal relationships). We also identified the individual differences in people's causal beliefs and observed the impacts of showing others' causal models when one is building his/her causal diagram and narratives. This work provides practical design implications for developing collaborative tools that facilitate capturing and sharing causal beliefs.

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5

Suzuki, Etsuji, Tomohiro Shinozaki e Eiji Yamamoto. "Causal Diagrams: Pitfalls and Tips". Journal of Epidemiology 30, n.º 4 (5 de abril de 2020): 153–62. http://dx.doi.org/10.2188/jea.je20190192.

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6

Picciotto*, Sally. "Causal Diagrams and Their Uses". ISEE Conference Abstracts 2014, n.º 1 (20 de outubro de 2014): 2901. http://dx.doi.org/10.1289/isee.2014.s-063.

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7

Mansournia, Mohammad A., Miguel A. Hernán e Sander Greenland. "Matched designs and causal diagrams". International Journal of Epidemiology 42, n.º 3 (junho de 2013): 860–69. http://dx.doi.org/10.1093/ije/dyt083.

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8

Greenland, Sander, Judea Pearl e James M. Robins. "Causal Diagrams for Epidemiologic Research". Epidemiology 10, n.º 1 (janeiro de 1999): 37–48. http://dx.doi.org/10.1097/00001648-199901000-00008.

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9

PEARL, JUDEA. "Causal diagrams for empirical research". Biometrika 82, n.º 4 (1995): 669–88. http://dx.doi.org/10.1093/biomet/82.4.669.

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10

COX, D. R., e NANNY WERMUTH. "Causal diagrams for empirical research". Biometrika 82, n.º 4 (1995): 688–89. http://dx.doi.org/10.1093/biomet/82.4.688.

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11

DAWID, A. P. "Causal diagrams for empirical research". Biometrika 82, n.º 4 (1995): 689–90. http://dx.doi.org/10.1093/biomet/82.4.689.

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12

FIENBERG, STEPHEN E., CLARK GLYMOUR e PETER SPIRTES. "Causal diagrams for empirical research". Biometrika 82, n.º 4 (1995): 690–92. http://dx.doi.org/10.1093/biomet/82.4.690.

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13

FREEDMAN, DAVID. "Causal diagrams for empirical research". Biometrika 82, n.º 4 (1995): 692–93. http://dx.doi.org/10.1093/biomet/82.4.692.

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14

IMBENS, GUIDO W., e DONALD B. RUBIN. "Causal diagrams for empirical research". Biometrika 82, n.º 4 (1995): 694–95. http://dx.doi.org/10.1093/biomet/82.4.694.

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15

ROBINS, JAMES M. "Causal diagrams for empirical research". Biometrika 82, n.º 4 (1995): 695–98. http://dx.doi.org/10.1093/biomet/82.4.695.

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16

ROSENBAUM, PAUL R. "Causal diagrams for empirical research". Biometrika 82, n.º 4 (1995): 698–99. http://dx.doi.org/10.1093/biomet/82.4.698.

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17

SHAFER, GLENN. "Causal diagrams for empirical research". Biometrika 82, n.º 4 (1995): 699–700. http://dx.doi.org/10.1093/biomet/82.4.699.

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18

SOBEL, MICHAEL E. "Causal diagrams for empirical research". Biometrika 82, n.º 4 (1995): 700–702. http://dx.doi.org/10.1093/biomet/82.4.700.

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19

PEARL, JUDEA. "Causal diagrams for empirical research". Biometrika 82, n.º 4 (1995): 702–10. http://dx.doi.org/10.1093/biomet/82.4.702.

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20

Shahar, Eyal, e Doron J. Shahar. "Causal diagrams and change variables". Journal of Evaluation in Clinical Practice 18, n.º 1 (12 de setembro de 2010): 143–48. http://dx.doi.org/10.1111/j.1365-2753.2010.01540.x.

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21

Richardson, George P. "Problems with causal-loop diagrams". System Dynamics Review 2, n.º 2 (1986): 158–70. http://dx.doi.org/10.1002/sdr.4260020207.

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22

Kunicki, Zachary J., Meghan L. Smith e Eleanor J. Murray. "A Primer on Structural Equation Model Diagrams and Directed Acyclic Graphs: When and How to Use Each in Psychological and Epidemiological Research". Advances in Methods and Practices in Psychological Science 6, n.º 2 (abril de 2023): 251524592311560. http://dx.doi.org/10.1177/25152459231156085.

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Many psychological researchers use some form of a visual diagram in their research processes. Model diagrams used with structural equation models (SEMs) and causal directed acyclic graphs (DAGs) can guide causal-inference research. SEM diagrams and DAGs share visual similarities, often leading researchers familiar with one to wonder how the other differs. This article is intended to serve as a guide for researchers in the psychological sciences and psychiatric epidemiology on the distinctions between these methods. We offer high-level overviews of SEMs and causal DAGs using a guiding example. We then compare and contrast the two methodologies and describe when each would be used. In brief, SEM diagrams are both a conceptual and statistical tool in which a model is drawn and then tested, whereas causal DAGs are exclusively conceptual tools used to help guide researchers in developing an analytic strategy and interpreting results. Causal DAGs are explicitly tools for causal inference, whereas the results of a SEM are only sometimes interpreted causally. A DAG may be thought of as a “qualitative schematic” for some SEMs, whereas SEMs may be thought of as an “algebraic system” for a causal DAG. As psychology begins to adopt more causal-modeling concepts and psychiatric epidemiology begins to adopt more latent-variable concepts, the ability of researchers to understand and possibly combine both of these tools is valuable. Using an applied example, we provide sample analyses, code, and write-ups for both SEM and causal DAG approaches.

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23

McGowan, Féidhlim P., e Peter D. Lunn. "Supporting decision-making in retirement planning: Do diagrams on Pension Benefit Statements help?" Journal of Pension Economics and Finance 19, n.º 3 (13 de fevereiro de 2019): 323–43. http://dx.doi.org/10.1017/s1474747219000015.

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AbstractThis paper investigates whether exposure to explanatory diagrams can affect a major financial decision. In a controlled experiment, participants were given Pension Benefit Statements with or without one or two diagrams, before answering incentivised questions that measured recall, comprehension and choice of contribution rate. The diagrams had at best a marginal influence on recall or comprehension. Nevertheless, a diagram relating contributions to income projections prompted more participants to advocate higher contributions, while both diagrams influenced the rationale participants gave for decisions. The implication is that although pension products remain hard to understand, diagrams may alter decisions by reinforcing relevant causal thinking.

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24

HU Xiaoxuan, JIANG Fan e XIA Wei. "Causal Influence Diagrams for Decision-making". Journal of Convergence Information Technology 8, n.º 5 (15 de março de 2013): 397–407. http://dx.doi.org/10.4156/jcit.vol8.issue5.46.

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25

Oates, Chris J., Jessica Kasza, Julie A. Simpson e Andrew B. Forbes. "Repair of Partly Misspecified Causal Diagrams". Epidemiology 28, n.º 4 (julho de 2017): 548–52. http://dx.doi.org/10.1097/ede.0000000000000659.

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26

Richardson, George P. "Problems in causal loop diagrams revisited". System Dynamics Review 13, n.º 3 (1997): 247–52. http://dx.doi.org/10.1002/(sici)1099-1727(199723)13:3<247::aid-sdr128>3.0.co;2-9.

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27

Lutomski, Jennifer E., A. Rogier T. Donders e René J. F. Melis. "Causal Diagrams to Better Understand Missingness". JAMA Pediatrics 168, n.º 2 (1 de fevereiro de 2014): 187. http://dx.doi.org/10.1001/jamapediatrics.2013.3650.

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28

Jacobs, Bart, Aleks Kissinger e Fabio Zanasi. "Causal inference via string diagram surgery". Mathematical Structures in Computer Science 31, n.º 5 (maio de 2021): 553–74. http://dx.doi.org/10.1017/s096012952100027x.

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Abstract Extracting causal relationships from observed correlations is a growing area in probabilistic reasoning, originating with the seminal work of Pearl and others from the early 1990s. This paper develops a new, categorically oriented view based on a clear distinction between syntax (string diagrams) and semantics (stochastic matrices), connected via interpretations as structure-preserving functors. A key notion in the identification of causal effects is that of an intervention, whereby a variable is forcefully set to a particular value independent of any prior propensities. We represent the effect of such an intervention as an endo-functor which performs ‘string diagram surgery’ within the syntactic category of string diagrams. This diagram surgery in turn yields a new, interventional distribution via the interpretation functor. While in general there is no way to compute interventional distributions purely from observed data, we show that this is possible in certain special cases using a calculational tool called comb disintegration. We demonstrate the use of this technique on two well-known toy examples: one where we predict the causal effect of smoking on cancer in the presence of a confounding common cause and where we show that this technique provides simple sufficient conditions for computing interventions which apply to a wide variety of situations considered in the causal inference literature; the other one is an illustration of counterfactual reasoning where the same interventional techniques are used, but now in a ‘twinned’ set-up, with two version of the world – one factual and one counterfactual – joined together via exogenous variables that capture the uncertainties at hand.

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29

Anand, Tara V., Adele H. Ribeiro, Jin Tian e Elias Bareinboim. "Causal Effect Identification in Cluster DAGs". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 10 (26 de junho de 2023): 12172–79. http://dx.doi.org/10.1609/aaai.v37i10.26435.

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Reasoning about the effect of interventions and counterfactuals is a fundamental task found throughout the data sciences. A collection of principles, algorithms, and tools has been developed for performing such tasks in the last decades. One of the pervasive requirements found throughout this literature is the articulation of assumptions, which commonly appear in the form of causal diagrams. Despite the power of this approach, there are significant settings where the knowledge necessary to specify a causal diagram over all variables is not available, particularly in complex, high-dimensional domains. In this paper, we introduce a new graphical modeling tool called cluster DAGs (for short, C-DAGs) that allows for the partial specification of relationships among variables based on limited prior knowledge, alleviating the stringent requirement of specifying a full causal diagram. A C-DAG specifies relationships between clusters of variables, while the relationships between the variables within a cluster are left unspecified, and can be seen as a graphical representation of an equivalence class of causal diagrams that share the relationships among the clusters. We develop the foundations and machinery for valid inferences over C-DAGs about the clusters of variables at each layer of Pearl's Causal Hierarchy - L1 (probabilistic), L2 (interventional), and L3 (counterfactual). In particular, we prove the soundness and completeness of d-separation for probabilistic inference in C-DAGs. Further, we demonstrate the validity of Pearl's do-calculus rules over C-DAGs and show that the standard ID identification algorithm is sound and complete to systematically compute causal effects from observational data given a C-DAG. Finally, we show that C-DAGs are valid for performing counterfactual inferences about clusters of variables.

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30

de Souza, Luanna K., e George E. A. Matsas. "Black-hole analog in vehicular traffic". American Journal of Physics 90, n.º 9 (setembro de 2022): 692–96. http://dx.doi.org/10.1119/5.0091957.

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We propose here a simple black-hole analog in vehicular-traffic dynamics. The corresponding causal diagram is determined by the propagation of the tail light flashes emitted by a convoy of cars on a highway. In addition to being a new black-hole analog, this illustrates how causal diagrams, so common in general relativity, may be useful in areas as unexpected as vehicular-traffic dynamics.

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31

Msaouel, Pavlos, Juhee Lee, Jose A. Karam e Peter F. Thall. "A Causal Framework for Making Individualized Treatment Decisions in Oncology". Cancers 14, n.º 16 (14 de agosto de 2022): 3923. http://dx.doi.org/10.3390/cancers14163923.

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We discuss how causal diagrams can be used by clinicians to make better individualized treatment decisions. Causal diagrams can distinguish between settings where clinical decisions can rely on a conventional additive regression model fit to data from a historical randomized clinical trial (RCT) to estimate treatment effects and settings where a different approach is needed. This may be because a new patient does not meet the RCT’s entry criteria, or a treatment’s effect is modified by biomarkers or other variables that act as mediators between treatment and outcome. In some settings, the problem can be addressed simply by including treatment–covariate interaction terms in the statistical regression model used to analyze the RCT dataset. However, if the RCT entry criteria exclude a new patient seen in the clinic, it may be necessary to combine the RCT data with external data from other RCTs, single-arm trials, or preclinical experiments evaluating biological treatment effects. For example, external data may show that treatment effects differ between histological subgroups not recorded in an RCT. A causal diagram may be used to decide whether external observational or experimental data should be obtained and combined with RCT data to compute statistical estimates for making individualized treatment decisions. We use adjuvant treatment of renal cell carcinoma as our motivating example to illustrate how to construct causal diagrams and apply them to guide clinical decisions.

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32

Dawid, A. P. "Influence Diagrams for Causal Modelling and Inference". International Statistical Review / Revue Internationale de Statistique 70, n.º 2 (agosto de 2002): 161. http://dx.doi.org/10.2307/1403901.

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33

Witmer, Jeff. "Simpson’s Paradox, Visual Displays, and Causal Diagrams". American Mathematical Monthly 128, n.º 7 (6 de agosto de 2021): 598–610. http://dx.doi.org/10.1080/00029890.2021.1932237.

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34

Williamson, Elizabeth J., Zoe Aitken, Jock Lawrie, Shyamali C. Dharmage, John A. Burgess e Andrew B. Forbes. "Introduction to causal diagrams for confounder selection". Respirology 19, n.º 3 (22 de janeiro de 2014): 303–11. http://dx.doi.org/10.1111/resp.12238.

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35

Hernan, M. A., e S. R. Cole. "Invited Commentary: Causal Diagrams and Measurement Bias". American Journal of Epidemiology 170, n.º 8 (15 de setembro de 2009): 959–62. http://dx.doi.org/10.1093/aje/kwp293.

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36

Dawid, A. P. "Influence Diagrams for Causal Modelling and Inference". International Statistical Review 70, n.º 2 (agosto de 2002): 161–89. http://dx.doi.org/10.1111/j.1751-5823.2002.tb00354.x.

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37

Aste, Andreas. "Two-Loop Diagrams in Causal Perturbation Theory". Annals of Physics 257, n.º 2 (julho de 1997): 158–204. http://dx.doi.org/10.1006/aphy.1997.5686.

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38

Crabolu, Gloria, Xavier Font e Sibel Eker. "Evaluating policy complexity with Causal Loop Diagrams". Annals of Tourism Research 100 (maio de 2023): 103572. http://dx.doi.org/10.1016/j.annals.2023.103572.

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39

Naser, M. Z. "Causal diagrams for civil and structural engineers". Structures 63 (maio de 2024): 106398. http://dx.doi.org/10.1016/j.istruc.2024.106398.

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40

Asnina, Erika. "Notion of causal relations of the topological functioning model". Applied Computer Systems 13, n.º 1 (8 de novembro de 2012): 68–73. http://dx.doi.org/10.2478/v10312-012-0009-z.

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Abstract - The paper discusses application of the topological functioning model (TFM) of the system for its automated transformation to behavioural specifications such as UML Activity Diagram, BPMN diagrams, scenarios, etc. The paper addresses a lack of formal specification of causal relations between functional features of the TFM by using inference means suggested by classical logic. The result is reduced human participation in the transformation as well as additional check of analysis and specification of the system.

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41

Rodgers, Mark, e Rosa Oppenheim. "Ishikawa diagrams and Bayesian belief networks for continuous improvement applications". TQM Journal 31, n.º 3 (8 de maio de 2019): 294–318. http://dx.doi.org/10.1108/tqm-11-2018-0184.

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Purpose In continuous improvement (CI) projects, cause-and-effect diagrams are used to qualitatively express the relationship between a given problem and its root causes. However, when data collection activities are limited, and advanced statistical analyses are not possible, practitioners need to understand causal relationships. The paper aims to discuss these issues. Design/methodology/approach In this research, the authors present a framework that combines cause-and-effect diagrams with Bayesian belief networks (BBNs) to estimate causal relationships in instances where formal data collection/analysis activities are too costly or impractical. Specifically, the authors use cause-and-effect diagrams to create causal networks, and leverage elicitation methods to estimate the likelihood of risk scenarios by means of computer-based simulation. Findings This framework enables CI practitioners to leverage qualitative data and expertise to conduct in-depth statistical analysis in the event that data collection activities cannot be fully executed. Furthermore, this allows CI practitioners to identify critical root causes of a given problem under investigation before generating solutions. Originality/value This is the first framework that translates qualitative insights from a cause-and-effect diagram into a closed-form relationship between inputs and outputs by means of BBN models, simulation and regression.

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Aste, A., e D. Trautmann. "Finite calculation of divergent self-energy diagrams". Canadian Journal of Physics 81, n.º 12 (1 de dezembro de 2003): 1433–45. http://dx.doi.org/10.1139/p03-103.

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Using dispersive techniques, it is possible to avoid ultraviolet divergences in the calculation of Feynman diagrams, making subsequent regularization of divergent diagrams unnecessary. We give a simple introduction to the most important features of such dispersive techniques in the framework of the so-called finite causal perturbation theory. The method is also applied to the "divergent" general massive two-loop sunrise self-energy diagram, where it leads directly to an analytic expression for the imaginary part of the diagram in accordance with the literature, whereas the real part can be obtained by a single integral dispersion relation. It is pointed out that dispersive methods have been known for decades and have been applied to several nontrivial Feynman diagram calculations.PACS Nos.: 11.10.z, 11.15.Bt, 12.20.Ds, 12.38.Bx

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Rahmat, Adi, Soesy Asiah Soesilowaty, Eni Nuraeni, Yogi Yogi, Imam Nugroho e Meilia Gemilawati. "REPRESENTASI MENTAL SISWA SMA DALAM MEMBACA GAMBAR BIOLOGI". Jurnal Pengajaran Matematika dan Ilmu Pengetahuan Alam 22, n.º 1 (26 de outubro de 2017): 68–76. http://dx.doi.org/10.18269/jpmipa.v22i1.8384.

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In learning biology, teachers often use visual media, such as diagrams, to help students understand the concept. This study was focused on how students’ mental representation (MR) when they read biological diagrams, either representation convention diagram or spatial isomorphism diagram. The student’s MR was analyzed using a worksheet modified from an MR measurement model of CNET protocol. This model involves three main components of the MR’s working memory: causal network, probability parameter, and utility parameter. Ninety 11th grade students of a senior high school in Bandung voluntarily involved in this study. Only 59 students, however, performed the worksheet completely. The result revealed that each student gave a different pattern of MR either when he/she read convention or spatial diagram. These differences could be traced since the student decides and orders the information elements and makes a causal network. Based on this result, we suggest that this method of MR analysis using a worksheet can be used to understand how student’s working memory works on MR building when they read a diagram. Understanding how the student can construct MR is a necessary platform to provide appropriate visual media that will help the student improve their learning achievement.

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Hamra, Ghassan, Jay Kaufman e Anjel Vahratian. "Model Averaging for Improving Inference from Causal Diagrams". International Journal of Environmental Research and Public Health 12, n.º 8 (11 de agosto de 2015): 9391–407. http://dx.doi.org/10.3390/ijerph120809391.

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Jupiter, Daniel C. "Causal Diagrams and Multivariate Analysis II: Precision Work". Journal of Foot and Ankle Surgery 53, n.º 6 (novembro de 2014): 829–31. http://dx.doi.org/10.1053/j.jfas.2014.08.023.

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Jupiter, Daniel C. "Causal Diagrams and Multivariate Analysis III: Confound It!" Journal of Foot and Ankle Surgery 54, n.º 1 (janeiro de 2015): 145–47. http://dx.doi.org/10.1053/j.jfas.2014.11.003.

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Gaskell, Amy L., e Jamie W. Sleigh. "An Introduction to Causal Diagrams for Anesthesiology Research". Anesthesiology 132, n.º 5 (1 de maio de 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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