Journal articles: 'Causal set'

1

Surya, Sumati. "Causal set topology." Theoretical Computer Science 405, no. 1-2 (October 2008): 188–97. http://dx.doi.org/10.1016/j.tcs.2008.06.033.

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He, Song, and David Rideout. "A causal set black hole." Classical and Quantum Gravity 26, no. 12 (June 2, 2009): 125015. http://dx.doi.org/10.1088/0264-9381/26/12/125015.

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Sverdlov, Roman. "Bosonic Fields in Causal Set Theory." International Journal of Theoretical Physics 60, no. 4 (March 31, 2021): 1481–506. http://dx.doi.org/10.1007/s10773-021-04772-6.

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Sorkin, Rafael D., and Yasaman K. Yazdi. "Entanglement entropy in causal set theory." Classical and Quantum Gravity 35, no. 7 (March 9, 2018): 074004. http://dx.doi.org/10.1088/1361-6382/aab06f.

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Bombelli, Luca, Joohan Lee, David Meyer, and Rafael D. Sorkin. "Space-time as a causal set." Physical Review Letters 59, no. 5 (August 3, 1987): 521–24. http://dx.doi.org/10.1103/physrevlett.59.521.

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6

Glaser, Lisa. "Causal set actions in various dimensions." Journal of Physics: Conference Series 306 (July 8, 2011): 012041. http://dx.doi.org/10.1088/1742-6596/306/1/012041.

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Major, Seth A., David Rideout, and Sumati Surya. "Spatial hypersurfaces in causal set cosmology." Classical and Quantum Gravity 23, no. 14 (July 5, 2006): 4743–51. http://dx.doi.org/10.1088/0264-9381/23/14/011.

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Philpott, L. "Particle simulations in causal set theory." Classical and Quantum Gravity 27, no. 4 (January 28, 2010): 042001. http://dx.doi.org/10.1088/0264-9381/27/4/042001.

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Criscuolo, A., and H. Waelbroeck. "Causal set dynamics: a toy model." Classical and Quantum Gravity 16, no. 6 (January 1, 1999): 1817–32. http://dx.doi.org/10.1088/0264-9381/16/6/315.

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10

Wüthrich, Christian, and Craig Callender. "What Becomes of a Causal Set?" British Journal for the Philosophy of Science 68, no. 3 (September 1, 2017): 907–25. http://dx.doi.org/10.1093/bjps/axv040.

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Drici, Z., F. A. Mcrae, and J. Vasundhara Devi. "Set differential equations with causal operators." Mathematical Problems in Engineering 2005, no. 2 (2005): 185–94. http://dx.doi.org/10.1155/mpe.2005.185.

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12

Dowker, Fay, and Lisa Glaser. "Causal set d'Alembertians for various dimensions." Classical and Quantum Gravity 30, no. 19 (September 11, 2013): 195016. http://dx.doi.org/10.1088/0264-9381/30/19/195016.

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Saravani, Mehdi, and Siavash Aslanbeigi. "On the causal set–continuum correspondence." Classical and Quantum Gravity 31, no. 20 (October 7, 2014): 205013. http://dx.doi.org/10.1088/0264-9381/31/20/205013.

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14

Carlip, S. "Dimensional reduction in causal set gravity." Classical and Quantum Gravity 32, no. 23 (November 9, 2015): 232001. http://dx.doi.org/10.1088/0264-9381/32/23/232001.

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15

Cunningham, William J., and Dmitri Krioukov. "Causal set generator and action computer." Computer Physics Communications 233 (December 2018): 123–33. http://dx.doi.org/10.1016/j.cpc.2018.06.008.

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Philpott, Lydia, Fay Dowker, and Rafael Sorkin. "Massless particle diffusion in causal set theory." Journal of Physics: Conference Series 174 (June 1, 2009): 012048. http://dx.doi.org/10.1088/1742-6596/174/1/012048.

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Sverdlov, Roman, and Luca Bombelli. "Gravity and matter in causal set theory." Classical and Quantum Gravity 26, no. 7 (March 19, 2009): 075011. http://dx.doi.org/10.1088/0264-9381/26/7/075011.

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Dowker, Fay, and Stav Zalel. "Evolution of universes in causal set cosmology." Comptes Rendus Physique 18, no. 3-4 (March 2017): 246–53. http://dx.doi.org/10.1016/j.crhy.2017.03.002.

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19

Eichhorn, Astrid, and Sebastian Mizera. "Spectral dimension in causal set quantum gravity." Classical and Quantum Gravity 31, no. 12 (May 27, 2014): 125007. http://dx.doi.org/10.1088/0264-9381/31/12/125007.

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20

Yu, Kui, Lin Liu, and Jiuyong Li. "A Unified View of Causal and Non-causal Feature Selection." ACM Transactions on Knowledge Discovery from Data 15, no. 4 (June 2021): 1–46. http://dx.doi.org/10.1145/3436891.

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In this article, we aim to develop a unified view of causal and non-causal feature selection methods. The unified view will fill in the gap in the research of the relation between the two types of methods. Based on the Bayesian network framework and information theory, we first show that causal and non-causal feature selection methods share the same objective. That is to find the Markov blanket of a class attribute, the theoretically optimal feature set for classification. We then examine the assumptions made by causal and non-causal feature selection methods when searching for the optimal feature set, and unify the assumptions by mapping them to the restrictions on the structure of the Bayesian network model of the studied problem. We further analyze in detail how the structural assumptions lead to the different levels of approximations employed by the methods in their search, which then result in the approximations in the feature sets found by the methods with respect to the optimal feature set. With the unified view, we can interpret the output of non-causal methods from a causal perspective and derive the error bounds of both types of methods. Finally, we present practical understanding of the relation between causal and non-causal methods using extensive experiments with synthetic data and various types of real-world data.

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21

Surya, Sumati, Nomaan X, and Yasaman K. Yazdi. "Entanglement entropy of causal set de Sitter horizons." Classical and Quantum Gravity 38, no. 11 (April 29, 2021): 115001. http://dx.doi.org/10.1088/1361-6382/abf279.

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22

Machet, Ludovico, and Jinzhao Wang. "On the horizon entropy of a causal set." Classical and Quantum Gravity 38, no. 8 (March 23, 2021): 085004. http://dx.doi.org/10.1088/1361-6382/abe957.

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23

Asato, Yu. "Black holes and singularities in causal set gravity." Classical and Quantum Gravity 36, no. 19 (September 11, 2019): 195008. http://dx.doi.org/10.1088/1361-6382/ab3c18.

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24

Dowker, Fay, Nazireen Imambaccus, Amelia Owens, Rafael Sorkin, and Stav Zalel. "A manifestly covariant framework for causal set dynamics." Classical and Quantum Gravity 37, no. 8 (March 19, 2020): 085003. http://dx.doi.org/10.1088/1361-6382/ab719c.

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25

Major, Seth, David Rideout, and Sumati Surya. "On recovering continuum topology from a causal set." Journal of Mathematical Physics 48, no. 3 (March 2007): 032501. http://dx.doi.org/10.1063/1.2435599.

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26

Moore, Cristopher. "Comment on ‘‘Space-time as a causal set’’." Physical Review Letters 60, no. 7 (February 15, 1988): 655. http://dx.doi.org/10.1103/physrevlett.60.655.

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27

Leva, Alberto, and Luca Bascetta. "Set point tracking optimisation by causal nonparametric modelling." Automatica 43, no. 11 (November 2007): 1984–91. http://dx.doi.org/10.1016/j.automatica.2007.04.002.

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28

Fomin, V. N. "Minimization of a functional over the set of causal operators of causal Hilbert space." Journal of Mathematical Sciences 77, no. 4 (December 1995): 3362–90. http://dx.doi.org/10.1007/bf02364867.

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29

Misangyi, Vilmos F., Thomas Greckhamer, Santi Furnari, Peer C. Fiss, Donal Crilly, and Ruth Aguilera. "Embracing Causal Complexity." Journal of Management 43, no. 1 (November 16, 2016): 255–82. http://dx.doi.org/10.1177/0149206316679252.

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Causal complexity has long been recognized as a ubiquitous feature underlying organizational phenomena, yet current theories and methodologies in management are for the most part not well-suited to its direct study. The introduction of the Qualitative Comparative Analysis (QCA) configurational approach has led to a reinvigoration of configurational theory that embraces causal complexity explicitly. We argue that the burgeoning research using QCA represents more than a novel methodology; it constitutes the emergence of a neo-configurational perspective to the study of management and organizations that enables a fine-grained conceptualization and empirical investigation of causal complexity through the logic of set theory. In this article, we identify four foundational elements that characterize this emerging neo-configurational perspective: (a) conceptualizing cases as set theoretic configurations, (b) calibrating cases’ memberships into sets, (c) viewing causality in terms of necessity and sufficiency relations between sets, and (d) conducting counterfactual analysis of unobserved configurations. We then present a comprehensive review of the use of QCA in management studies that aims to capture the evolution of the neo-configurational perspective among management scholars. We close with a discussion of a research agenda that can further this neo-configurational approach and thereby shift the attention of management research away from a focus on net effects and towards examining causal complexity.

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30

Duffy, Callum F., Joshua Y. L. Jones, and Yasaman K. Yazdi. "Entanglement entropy of disjoint spacetime intervals in causal set theory." Classical and Quantum Gravity 39, no. 7 (March 10, 2022): 075017. http://dx.doi.org/10.1088/1361-6382/ac5493.

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Abstract A more complete understanding of entanglement entropy in a covariant manner could inform the search for quantum gravity. We build on work in this direction by extending previous results to disjoint regions in 1 + 1D. We investigate the entanglement entropy of a scalar field in disjoint intervals within the causal set framework, using the spacetime commutator and correlator, i Δ and W (or the Pauli–Jordan and Wightman functions). A new truncation scheme for disjoint causal diamonds is presented, which follows from the single diamond truncation scheme. We investigate setups including two and three disjoint causal diamonds, as well as a single causal diamond that shares a boundary with a larger global causal diamond. In all the cases that we study, our results agree with the expected area laws. In addition, we study the mutual information in the two disjoint diamond setup. The ease of our calculations indicate our methods to be a useful tool for numerically studying such systems. We end with a discussion of some of the strengths and future applications of the spacetime formulation we use in our entanglement entropy computations, both in causal set theory and in the continuum.

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31

BRIGHTWELL, GRAHAM, and MALWINA LUCZAK. "Order-Invariant Measures on Fixed Causal Sets." Combinatorics, Probability and Computing 21, no. 3 (January 19, 2012): 330–57. http://dx.doi.org/10.1017/s0963548311000721.

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A causal set is a countably infinite poset in which every element is above finitely many others; causal sets are exactly the posets that have a linear extension with the order-type of the natural numbers; we call such a linear extension a natural extension. We study probability measures on the set of natural extensions of a causal set, especially those measures having the property of order-invariance: if we condition on the set of the bottom k elements of the natural extension, each feasible ordering among these k elements is equally likely. We give sufficient conditions for the existence and uniqueness of an order-invariant measure on the set of natural extensions of a causal set.

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32

Long, Susan B., and Richard H. Evans. "Matching Attribute set and Attitude Model." Psychological Reports 60, no. 3_part_2 (June 1987): 1087–96. http://dx.doi.org/10.1177/0033294187060003-214.1.

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The attribute set/attitude model relationship recently proposed by Myers and Shocker was examined using correlational and (LISREL) causal analysis. Compared against more traditional single-attitude model approaches (Ahtola, Adequacy-Importance, and Fishbein), Myers and Shocker's mixed model appears promising. It matched the attitude model which best predicted behavioral intentions for two out of three product attribute sets, and along with the Fishbein model, produced a satisfactory fit with the hypothesized causal structure. However, when the alternative attitude models were compared using a multiple indicator approach, results suggest these models do not appear to be directly measuring the same underlying attribute sets.

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33

Du, Yunzhou, and Qiuchen Liu. "Testing Explicit Mediation Models by Set-Theoretic Causal Complexity." Academy of Management Proceedings 2021, no. 1 (August 2021): 14898. http://dx.doi.org/10.5465/ambpp.2021.14898abstract.

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34

Krugly, Alexey L., and Ivan A. Tserkovnikov. "An example of numerical simulation in causal set dynamics." Journal of Physics: Conference Series 442 (June 10, 2013): 012042. http://dx.doi.org/10.1088/1742-6596/442/1/012042.

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35

Eichhorn, Astrid, Sebastian Mizera, and Sumati Surya. "Echoes of asymptotic silence in causal set quantum gravity." Classical and Quantum Gravity 34, no. 16 (July 20, 2017): 16LT01. http://dx.doi.org/10.1088/1361-6382/aa7d1b.

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36

Glaser, Lisa, Denjoe O’Connor, and Sumati Surya. "Finite size scaling in 2d causal set quantum gravity." Classical and Quantum Gravity 35, no. 4 (January 15, 2018): 045006. http://dx.doi.org/10.1088/1361-6382/aa9540.

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37

Dowker, Fay, and Sumati Surya. "Observables in extended percolation models of causal set cosmology." Classical and Quantum Gravity 23, no. 4 (February 7, 2006): 1381–90. http://dx.doi.org/10.1088/0264-9381/23/4/018.

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38

Glaser, Lisa. "A closed form expression for the causal set d’Alembertian." Classical and Quantum Gravity 31, no. 9 (April 16, 2014): 095007. http://dx.doi.org/10.1088/0264-9381/31/9/095007.

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39

Neal, Zachary. "Creative Employment and Jet Set Cities: Disentangling Causal Effects." Urban Studies 49, no. 12 (January 5, 2012): 2693–709. http://dx.doi.org/10.1177/0042098011431282.

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40

Triantafillou, Sofia, and Greg Cooper. "Learning Adjustment Sets from Observational and Limited Experimental Data." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 11 (May 18, 2021): 9940–48. http://dx.doi.org/10.1609/aaai.v35i11.17194.

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Estimating causal effects from observational data is not always possible due to confounding. Identifying a set of appropriate covariates (adjustment set) and adjusting for their influence can remove confounding bias; however, such a set is often not identifiable from observational data alone. Experimental data allow unbiased causal effect estimation, but are typically limited in sample size and can therefore yield estimates of high variance. Moreover, experiments are often performed on a different (specialized) population than the population of interest. In this work, we introduce a method that combines large observational and limited experimental data to identify adjustment sets and improve the estimation of causal effects for a target population. The method scores an adjustment set by calculating the marginal likelihood for the experimental data given an observationally-derived causal effect estimate, using a putative adjustment set. The method can make inferences that are not possible using constraint-based methods. We show that the method can improve causal effect estimation, and can make additional inferences when compared to state-of-the-art methods.

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41

Eichhorn, Astrid, Sumati Surya, and Fleur Versteegen. "Spectral dimension on spatial hypersurfaces in causal set quantum gravity." Classical and Quantum Gravity 36, no. 23 (November 1, 2019): 235013. http://dx.doi.org/10.1088/1361-6382/ab47cd.

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42

Sorkin, Rafael D. "Scalar Field Theory on a Causal Set in Histories form." Journal of Physics: Conference Series 306 (July 8, 2011): 012017. http://dx.doi.org/10.1088/1742-6596/306/1/012017.

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43

Henson, Joe. "Constructing an interval of Minkowski space from a causal set." Classical and Quantum Gravity 23, no. 4 (February 7, 2006): L29—L35. http://dx.doi.org/10.1088/0264-9381/23/4/l02.

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44

Yue, Zongliang, Michael T. Neylon, Thanh Nguyen, Timothy Ratliff, and Jake Y. Chen. "“Super Gene Set” Causal Relationship Discovery from Functional Genomics Data." IEEE/ACM Transactions on Computational Biology and Bioinformatics 15, no. 6 (November 1, 2018): 1991–98. http://dx.doi.org/10.1109/tcbb.2018.2858755.

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45

Baumgartner, Michael, and Mathias Ambühl. "Causal modeling with multi-value and fuzzy-set Coincidence Analysis." Political Science Research and Methods 8, no. 3 (November 5, 2018): 526–42. http://dx.doi.org/10.1017/psrm.2018.45.

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AbstractCoincidence Analysis (CNA) is a configurational comparative method of causal data analysis that is related to Qualitative Comparative Analysis (QCA) but, contrary to the latter, is custom-built for analyzing causal structures with multiple outcomes. So far, however, CNA has only been capable of processing dichotomous variables, which greatly limited its scope of applicability. This paper generalizes CNA for multi-value variables as well as continuous variables whose values are interpreted as membership scores in fuzzy sets. This generalization comes with a major adaptation of CNA’s algorithmic protocol, which, in an extended series of benchmark tests, is shown to give CNA an edge over QCA not only with respect to multi-outcome structures but also with respect to the analysis of non-ideal data stemming from single-outcome structures. The inferential power of multi-value and fuzzy-set CNA is made available to end users in the newest version of the R package cna.

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46

Arageorgis, Aristidis. "Spacetime as a causal set: Universe as a growing block?" Belgrade Philosophical Annual, no. 29 (2016): 33–55. http://dx.doi.org/10.5937/bpa1629033a.

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47

Surya, Sumati. "Evidence for the continuum in 2D causal set quantum gravity." Classical and Quantum Gravity 29, no. 13 (June 6, 2012): 132001. http://dx.doi.org/10.1088/0264-9381/29/13/132001.

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48

Belenchia, Alessio. "Universal behavior of generalized causal set d’Alembertians in curved spacetime." Classical and Quantum Gravity 33, no. 13 (June 14, 2016): 135011. http://dx.doi.org/10.1088/0264-9381/33/13/135011.

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49

Ash, Avner, and Patrick McDonald. "Moment problems and the causal set approach to quantum gravity." Journal of Mathematical Physics 44, no. 4 (April 2003): 1666–78. http://dx.doi.org/10.1063/1.1519668.

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50

ORTIGUEIRA, MANUEL D., MARGARITA RIVERO, and JUAN J. TRUJILLO. "THE INCREMENTAL RATIO BASED CAUSAL FRACTIONAL CALCULUS." International Journal of Bifurcation and Chaos 22, no. 04 (April 2012): 1250078. http://dx.doi.org/10.1142/s0218127412500782.

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The generalized incremental ratio fractional derivative is revised and its main properties deduced. It is shown that in the case of analytic functions, it enjoys some interesting properties like: linearity and causality and has a semi-group structure. Some simple examples are presented. The enlargement of the set of functions for which the group properties of the fractional derivative are valid is done. With this, it is shown that some well-known results are valid in a more general set-up. Some examples are presented.

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Journal articles on the topic 'Causal set'

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