Identifying Causal Structures from Cyberstalking: Behaviors Severity and Association

Shkurte Luma-Osmani; Florije Ismaili; Pankaj Pathak; Xhemal Zenuni

doi:10.24138/jcomss-2021-0139

Interacting Aliens: Inferring Causal Structures From Covariational Data

PsycEXTRA Dataset ◽

10.1037/e527342012-863 ◽

2007 ◽

Author(s):

Ralf Mayrhofer ◽

Michael R. Waldmann ◽

York Hagmayer

Keyword(s):

Causal Structures

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The orbitofrontal cartographer

10.31234/osf.io/4mrxy ◽

2020 ◽

Cited By ~ 1

Author(s):

Matthew P H Gardner ◽

Geoffrey Schoenbaum

Keyword(s):

Orbitofrontal Cortex ◽

Economic Value ◽

Cognitive Map ◽

Credit Assignment ◽

General Consensus ◽

Future Events ◽

Causal Structures ◽

Precise Content ◽

Successes And Failures

Theories of orbitofrontal cortex (OFC) function have evolved substantially over the last few decades. There is now a general consensus that the OFC is important for predicting aspects of future events and for using these predictions to guide behavior. Yet the precise content of these predictions and the degree to which OFC contributes to agency contingent upon them has become contentious, with several plausible theories advocating different answers to these questions. In this review we will focus on three of these ideas - the economic value, credit assignment, and cognitive map hypotheses – describing both their successes and failures. We will propose that these failures hint at a more nuanced role for the OFC in supporting the proposed functions when an underlying model or map of the causal structures in the environment must be constructed or updated.

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Performance of approaches relying on multidimensional intermediary data to decipher causal relationships between the exposome and health: A simulation study under various causal structures

Environment International ◽

10.1016/j.envint.2021.106509 ◽

2021 ◽

Vol 153 ◽

pp. 106509

Author(s):

Solène Cadiou ◽

Xavier Basagaña ◽

Juan R. Gonzalez ◽

Johanna Lepeule ◽

Martine Vrijheid ◽

...

Keyword(s):

Simulation Study ◽

Causal Relationships ◽

Causal Structures

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Cyclic quantum causal models

Nature Communications ◽

10.1038/s41467-020-20456-x ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Jonathan Barrett ◽

Robin Lorenz ◽

Ognyan Oreshkov

Keyword(s):

Causal Reasoning ◽

Causal Structure ◽

Bell Inequalities ◽

Quantum Correlations ◽

Quantum Systems ◽

Causal Models ◽

Causal Order ◽

Quantum Processes ◽

Causal Explanations ◽

Causal Structures

AbstractCausal reasoning is essential to science, yet quantum theory challenges it. Quantum correlations violating Bell inequalities defy satisfactory causal explanations within the framework of classical causal models. What is more, a theory encompassing quantum systems and gravity is expected to allow causally nonseparable processes featuring operations in indefinite causal order, defying that events be causally ordered at all. The first challenge has been addressed through the recent development of intrinsically quantum causal models, allowing causal explanations of quantum processes – provided they admit a definite causal order, i.e. have an acyclic causal structure. This work addresses causally nonseparable processes and offers a causal perspective on them through extending quantum causal models to cyclic causal structures. Among other applications of the approach, it is shown that all unitarily extendible bipartite processes are causally separable and that for unitary processes, causal nonseparability and cyclicity of their causal structure are equivalent.

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The Inflation Technique Completely Solves the Causal Compatibility Problem

Journal of Causal Inference ◽

10.1515/jci-2018-0008 ◽

2020 ◽

Vol 8 (1) ◽

pp. 70-91 ◽

Cited By ~ 1

Author(s):

Miguel Navascués ◽

Elie Wolfe

Keyword(s):

Linear Programming ◽

Latent Variables ◽

Causal Explanation ◽

Causal Structure ◽

Categorical Variables ◽

False Negatives ◽

The Given ◽

Causal Structures ◽

Compatibility Question ◽

Structure Graph

AbstractThe causal compatibility question asks whether a given causal structure graph — possibly involving latent variables — constitutes a genuinely plausible causal explanation for a given probability distribution over the graph’s observed categorical variables. Algorithms predicated on merely necessary constraints for causal compatibility typically suffer from false negatives, i.e. they admit incompatible distributions as apparently compatible with the given graph. In 10.1515/jci-2017-0020, one of us introduced the inflation technique for formulating useful relaxations of the causal compatibility problem in terms of linear programming. In this work, we develop a formal hierarchy of such causal compatibility relaxations. We prove that inflation is asymptotically tight, i.e., that the hierarchy converges to a zero-error test for causal compatibility. In this sense, the inflation technique fulfills a longstanding desideratum in the field of causal inference. We quantify the rate of convergence by showing that any distribution which passes the nth-order inflation test must be $\begin{array}{} \displaystyle {O}{\left(n^{{{-}{1}}/{2}}\right)} \end{array}$-close in Euclidean norm to some distribution genuinely compatible with the given causal structure. Furthermore, we show that for many causal structures, the (unrelaxed) causal compatibility problem is faithfully formulated already by either the first or second order inflation test.

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