Causal Order Research Articles

The stratigraphic biography of an archaeological site. Timing depositional events

AbstractThe stratigraphic sequence of an archaeological site constitutes its biography, which details what was deposited, when it was deposited, and how the deposition occurred. However, many current methods of recording archaeological stratigraphy do not allow for a full reconstruction of a site's biography. This paper introduces a theoretical framework for analysing a site's formation and deformation processes, emphasizing the integration of different temporal, spatial, geological, and archaeological information elements. This approach offers a way to study change and dynamics by deciphering successive episodes, depositional events, their temporal and causal ordering, and, when possible, the speed and rhythm of such depositions. The integration of micromorphological data and absolute dating can increase the chronological resolution of a site's biography by formally defining the temporal boundaries of depositional events. Additionally, the use of multidimensional geometric analysis of occupational floors and sedimentary volumes enhances the understanding of the complex relationships between stratigraphic depth and temporality. These tools enable archaeologists to create multidimensional visualizations, make inferences, and enhance interpretations of archaeological sites.

Enhancing quantum state discrimination with indefinite causal order

Abstract The standard quantum state discrimination problem can be understood as a communication scenario involving a sender and a receiver following these three steps: (i) the sender encodes information in pre-agreed quantum states, (ii) sends them over a noiseless channel, and (iii) the receiver decodes the information by performing appropriate measurements on the received states. In a practical setting, however, the channel is not only noisy but often also unknown, thus altering the states and making optimal decoding generally not possible. In this work, we study this noisy discrimination scenario using a protocol based on indefinite causal order. To this end, we consider the quantum switch and define its higher-order generalisations, which we call superswitches. We find that, for certain channels and ensembles, the guessing probability can be significantly improved compared to both single- and multiple-copy state discrimination.

Modeling, simulation, and evaluation of causal order assurance techniques in causal broadcast algorithms using timed colored petri nets

Modeling, simulation, and evaluation of causal order assurance techniques in causal broadcast algorithms using timed colored petri nets

Quantum switch instabilities with an open control

The superposition of causal orders shows promise in various quantum technologies. However, the fragility of quantum systems arising from environmental interactions, leading to dissipative behavior and irreversibility, demands a deeper understanding of the possible instabilities in the coherent control of causal orders. In this work, we employ a collisional model to investigate the impact of an open control system on the generation of interference between two causal orders. We present the environmental instabilities for the switch of two arbitrary quantum operations and examine the influence of environmental temperature on each potential outcome of control post-selection. Additionally, we explore how environmental instabilities affect protocol performance, including switching between mutually unbiased measurement observables and refrigeration powered by causal order superposition, providing insights into broader implications.

Controlling the narrative: the relationship between narrative ability and executive functioning in children with developmental language disorder.

Children with developmental language disorder (DLD) experience problems in language comprehension and/or production. In particular, storytelling or narrative ability is often impaired, as this type of discourse involves all domains of language. These problems may lead to a lower quality of social interaction and mental health. Moreover, problems in oral narrative ability during early development have a negative effect on later literacy. However, telling a story involves more than language alone. Executive functioning is thought to play an important part in stimulating narrative ability, as linguistic utterances need to be planned in a temporal and causal order, and switching is needed between multiple characters and events in the story. Research has shown that children with DLD experience problems with executive functioning, independent of their language ability. Thus, the difficulties in storytelling may be caused by both impaired language and executive functioning, as both domains follow hierarchical developmental paths during the early childhood years. In this review, we discuss three components of narrative ability (comprehension, production of macrostructure and production of microstructure) and how they may be interconnected to the three core components of executive functioning (working memory, switching and inhibition) and attention. This review shows that updating and monitoring information in working memory plays an important part in all three components of narrative ability, across multiple studies. This result may give direction in the development of narrative assessment and intervention, and urge further research to disentangle the interplay between language and executive control in DLD.

Quantum machine learning with indefinite causal order

Quantum machine learning with indefinite causal order

Illuminating Causality: The Role of Light in Thomas Aquinas’s Metaphysics of Being

In this article, we hold a strong thesis, i.e., that light is the conceptual resource that best explains the creative act in Thomas Aquinas’s metaphysics. Thus, inasmuch as being is the luminous act of the Uncreated Being, it bears resemblance to the Uncreated Being in its very principle. In this sense, we seek to present the role of light, in its meaning as manifestation, as an act that belongs to being. Indeed, in contrast with some contemporary interpretations, which place light within the order of formal causality, we argue that light, in its belonging to Aquinas’s participatory metaphysics, pertains to the order of efficient causality. Therefore, we hold that light has strong explanatory pretensions in Aquinas’ thought. Accordingly, after an introduction (1), this article is divided into the following sections: (2) The Meaning of Divine Causality; (3) Being as Light; (4) The Manifestative Character of Light; (5) Light Images: Analogies and Metaphors; and (6) Conclusions.

Spin-2 Green’s functions on Kerr in radiation gauge

Abstract We construct retarded and advanced Green’s functions for gravitational perturbations in Kerr in an ingoing radiation gauge. Our Green’s functions have a frequency domain piece that has previously been obtained by Ori (2003 Phys. Rev. D 67) based on the Chrzanowski-Cohen-Kegeles metric reconstruction method. As is well known, this piece by itself is not sufficient to obtain an actual Green’s function. We show how to complete it with a piece based on a method by Green et al (2020 Class. Quantum Grav. 37). The completion piece has a completely explicit form in the time-domain and is supported on pairs of points on the same outgoing principal null geodesic which are in the appropriate causal order. We expect our Green’s functions to be useful for gravitational self-force calculations and other perturbation problems on Kerr spacetime.

Transcriptome-Wide Root Causal Inference.

Root causal genes correspond to the first gene expression levels perturbed during pathogenesis by genetic or non-genetic factors. Targeting root causal genes has the potential to alleviate disease entirely by eliminating pathology near its onset. No existing algorithm discovers root causal genes from observational data alone. We therefore propose the Transcriptome-Wide Root Causal Inference (TWRCI) algorithm that identifies root causal genes and their causal graph using a combination of genetic variant and unperturbed bulk RNA sequencing data. TWRCI uses a novel competitive regression procedure to annotate cis and trans-genetic variants to the gene expression levels they directly cause. The algorithm simultaneously recovers a causal ordering of the expression levels to pinpoint the underlying causal graph and estimate root causal effects. TWRCI outperforms alternative approaches across a diverse group of metrics by directly targeting root causal genes while accounting for distal relations, linkage disequilibrium, patient heterogeneity and widespread pleiotropy. We demonstrate the algorithm by uncovering the root causal mechanisms of two complex diseases, which we confirm by replication using independent genome-wide summary statistics.

Substance use patterns and mental health comorbidities in youth with a history of depression or suicidality: Findings from TX-YDSRN

BackgroundThere is a robust relationship between depression and substance use in youth, with higher levels of substance use associated with greater depressive symptomatology. However, previous research has examined individual substances, without consideration of psychiatric comorbidities. Here, we investigate patterns of substance use among depressed and/or suicidal youth within the context of psychiatric comorbidities. Methods945 youth with depression and/or suicidality from the Texas Youth Depression and Suicide Research Network (TX-YDSRN) were assessed for current use of alcohol, nicotine, cannabis, and other drugs and comorbid psychiatric diagnoses. We used latent class analysis to identify patterns of past-year substance use, then examined if demographics or psychiatric disorders predicted class membership. ResultsWe identified three patterns of substance use: non-use (63.4 %), moderate likelihood of using alcohol, nicotine and cannabis (23.8 %), and high likelihood of using all substances (12.7 %). Compared to non-users, individuals in the moderate and high likelihood classes were more likely to be older. Individuals in the high likelihood class were more likely to have a substance use disorder, ADHD, and higher suicidality scores. LimitationsWe cannot ascertain the causal or temporal ordering of substance use and psychiatric diagnoses due to the cross-sectional nature of the study. ConclusionsUsing a brief, self-report measure of substance use, we identified three classes of substance users differing in probability of past-year use, which were predicted by older age and some psychiatric comorbidities. While research on universal screening of substance use in youth remains limited, we discuss who may benefit from such screening among depressed youth.

Assessing the causal relationship between metabolic biomarkers and coronary artery disease by Mendelian randomization studies

The development of coronary artery disease (CAD) is significantly affected by impaired endocrine and metabolic status. Under this circumstance, improved prevention and treatment of CAD may result from knowing the connection between metabolites and CAD. This study aims to delve into the causal relationship between human metabolic biomarkers and CAD by using two-sample Mendelian randomization (MR). Utilizing two-sample bidirectional MR analysis, we assessed the correlation between 1400 blood metabolites and CAD, and the metabolites data from the CLSA, encompassing 8299 participants. Metabolite analysis identified 1091 plasma metabolites and 309 ratios as instrumental variables. To evaluate the causal link between metabolites and CAD, we analyzed three datasets: ebi-a-GCST005195 (547,261 European & East Asian samples), bbj-a-159 (29,319 East Asian CAD cases & 183,134 East Asian controls), and ebi-a-GCST005194 (296,525 European & East Asian samples). To estimate causal links, we utilized the IVW method. To conduct sensitivity analysis, we used MR-Egger, Weighted Median, and MR-PRESSO. Additionally, we employed MR-Egger interception and Cochran’s Q statistic to assess potential heterogeneity and pleiotropy. What’s more, replication and reverse analyses were performed to verify the reliability of the results and the causal order between metabolites and disease. Furthermore, we conducted a pathway analysis to identify potential metabolic pathways. 59 blood metabolites and 27 metabolite ratios nominally associated with CAD (P < 0.05) were identified by IVW analysis method. A total of four known blood metabolites, namely beta-hydroxyisovaleroylcarnitine (OR 1.06, 95% CI 1.027–1.094, FDR 0.07), 1-palmitoyl-2-arachidonoyl (OR 1.07, 95% CI 1.029–1.110, FDR 0.09), 1-stearoyl-2- docosahexaenoyl (OR 1.07, 95% CI 1.034–1.113, FDR 0.07) and Linoleoyl-arachidonoyl-glycerol, (OR 1.07, 95% CI 1.036–1.105, FDR 0.05), and two metabolite ratios, namely spermidine to N-acetylputrescine ratio (OR 0.94, 95% CI 0.903–0.972, FDR 0.09) and benzoate to linoleoyl-arachidonoyl-glycerol ratio (OR 0.87, 95% CI 0.879–0.962, FDR 0.07), were confirmed as having a significant causal relationship with CAD, after correcting for the FDR method (p < 0. 1). A causal relationship was found to be established between beta -hydroxyisovalerylcarnitine and CAD with the validation in other two datasets. Moreover, multiple metabolic pathways were discovered to be associated with CAD. Our study supports the hypothesis that metabolites have an impact on CAD by demonstrating a causal relationship between human metabolites and CAD. This study is important for new strategies for the prevention and treatment of CAD.

What Kind of ‘God’ do Hindu Arguments for the Divine Show? Five Novel Divine Attributes of Brahman

This article describes the ultimate ground of reality, Brahman, as a single power unfolding in concert in all things. It uses counterfactual argumentation to imply that a cosmos must consist of telic causal orders or manifested ‘powers’ as its most granular building block – and that they must be unified into a single whole. It is based on an argument for a single causally-conditioning substrate of all things recorded in India’s classical Sāṃkhya Kārikā and Brahma Sūtras; this was used by scholastic Vedāntic thinkers including the non-dualist Śaṃkara and the ‘transformationist’ Bhedābheda thinker Śrīnivāsa. It takes up arguments for satkārya, the pre-existence of a thing’s various transformations in potentio within its substrate, and employs them to paint a picture of reality as an ordered yet entangled pattern of causal trajectories. These manifest as the changing world we know. While Vedānta’s main motivation for making the arguments was to prove monism, this conception of an ‘immanent cause’ of all was also seen as divine. We see how this accords with moves in contemporary analytic philosophy to shift away from a broadly Humean model of constant conjunctions (reminiscent of key Buddhist approaches) toward a Neo-Aristotelian ‘metaphysics of powers’. But we also indicate how it may go further than most modern powers-metaphysicians in proposing a deeper connection between powers and what it is for there to be a cosmos at all.

Discovering Root Causal Genes with High Throughput Perturbations.

Root causal gene expression levels - or root causal genes for short - correspond to the initial changes to gene expression that generate patient symptoms as a downstream effect. Identifying root causal genes is critical towards developing treatments that modify disease near its onset, but no existing algorithms attempt to identify root causal genes from data. RNA-sequencing (RNA-seq) data introduces challenges such as measurement error, high dimensionality and non-linearity that compromise accurate estimation of root causal effects even with state-of-the-art approaches. We therefore instead leverage Perturb-seq, or high throughput perturbations with single cell RNA-seq readout, to learn the causal order between the genes. We then transfer the causal order to bulk RNA-seq and identify root causal genes specific to a given patient for the first time using a novel statistic. Experiments demonstrate large improvements in performance. Applications to macular degeneration and multiple sclerosis also reveal root causal genes that lie on known pathogenic pathways, delineate patient subgroups and implicate a newly defined omnigenic root causal model.

Experimentally Demonstrating Indefinite Causal Order Algorithms to Solve the Generalized Deutsch's Problem

AbstractDeutsch's algorithm is the first quantum algorithm to demonstrate an advantage over classical algorithms. Here, Deutsch's problem is generalized to functions and a quantum algorithm with an indefinite causal order is proposed to solve this problem. The algorithm not only reduces the number of queries to the black box by half compared to the classical algorithm, but also significantly decreases the complexity of the quantum circuit and the number of required quantum gates compared to the generalized Deutsch's algorithm. The algorithm is experimentally demonstrated in a stable Sagnac loop interferometer with a common path, which overcomes the obstacles of both phase instability and low fidelity of the Mach–Zehnder interferometer. The experimental results show both ultrahigh and robust success probabilities . This study opens a path toward solving practical problems with indefinite cause‐order quantum circuits.

Revisiting Dynamics of Quantum Causal Structures-When Can Causal Order Evolve?

Recently, there has been substantial interest in studying the dynamics of quantum theory beyond that of states, in particular, the dynamics of channels, measurements, and higher-order transformations. Castro-Ruiz et al. pursues this using the process-matrix formalism, together with a definition of the possible dynamics of such process matrices, and focusing especially on the question of evolution of causal structures. One of its major conclusions is a strong theorem saying that within the formalism, under continuous and reversible transformations, the causal order between operations must be preserved. Our result here challenges that of Castro-Ruiz et al.: if one is to take into account a full picture of the physical evolution of operations within the standard quantum-mechanical formalism, then the conclusion of Castro-Ruiz et al. does not hold. That is, we show that under certain continuous and reversible dynamics, the causal order between operations is not necessarily preserved. We moreover identify and analyse the root of this apparent contradiction, specifically, that the commonly accepted and widely applied framework of higher-order processes, whilst mathematically sound, is not always appropriate for drawing conclusions on physical dynamics. Finally, we show how to reconcile the elements of the whole picture following the intuition based on entanglement processing by local operations and classical communication.

Phase estimation for a single-mode photon influenced by completely thermalizing channels with indefinite causal order

Phase estimation for a single-mode photon is investigated when the photon is influenced by two completely thermalizing channels with indefinite causal order, where a phase shift takes place between the two channels. Accuracy of the phase estimation is evaluated in terms of the quantum Fisher information. It is shown that although the channels completely destruct the phase information of the photon, the phase estimation is possible with the assistance of the indefinite causal order.

Quantum query complexity of Boolean functions under indefinite causal order

The standard model of quantum circuits assumes operations are applied in a fixed sequential “causal” order. In recent years, the possibility of relaxing this constraint to obtain causally indefinite computations has received significant attention. The quantum switch, for example, uses a quantum system to coherently control the order of operations. Several computational and information-theoretical advantages have been demonstrated, raising questions as to whether advantages can be obtained in a more unified complexity theoretic framework. In this paper, we approach this problem by studying the query complexity of Boolean functions under general higher-order quantum computations. To this end, we generalize the framework of query complexity from quantum circuits to quantum supermaps to compare different models on an equal footing. We show that the recently introduced class of quantum circuits with quantum control of causal order cannot lead to any reduction in query complexity, and that any potential advantage arising from causally indefinite supermaps can be bounded by the polynomial method, as is the case with quantum circuits. Nevertheless, we find some functions for which the minimum error with which they can be computed using two queries is strictly lower when exploiting causally indefinite supermaps. Published by the American Physical Society 2024

Clarifying the Relationship Between Political Consumerism and Political Persuasion Over Time

Political consumerism has emerged as a popular form of participation among lay citizens. For the most part, research has delved into the main antecedents that explain this type of consumption. However, research has generally neglected to consider political consumerism as an independent variable, and its role influencing and igniting other forms of political behavior. This study theoretically argues that politically motivated consumerism will incite people to generally seek to persuade others of political issues. Using causal order tests and Random Intercept Cross-lagged Panel Structural Equation Models from a two-wave panel survey from the United States, the study shows robust empirical evidence that this is the case.

Memory in quantum processes with indefinite time direction and causal order

Memory in quantum processes with indefinite time direction and causal order

Characterising transformations between quantum objects, &amp;apos;completeness&amp;apos; of quantum properties, and transformations without a fixed causal order

Many fundamental and key objects in quantum mechanics are linear mappings between particular affine/linear spaces. This structure includes basic quantum elements such as states, measurements, channels, instruments, non-signalling channels and channels with memory, and also higher-order operations such as superchannels, quantum combs, n-time processes, testers, and process matrices which may not respect a definite causal order. Deducing and characterising their structural properties in terms of linear and semidefinite constraints is not only of foundational relevance, but plays an important role in enabling the numerical optimisation over sets of quantum objects and allowing simpler connections between different concepts and objects. Here, we provide a general framework to deduce these properties in a direct and easy to use way. While primarily guided by practical quantum mechanical considerations, we also extend our analysis to mappings between general linear/affine spaces and derive their properties, opening the possibility for analysing sets which are not explicitly forbidden by quantum theory, but are still not much explored. Together, these results yield versatile and readily applicable tools for all tasks that require the characterisation of linear transformations, in quantum mechanics and beyond. As an application of our methods, we discuss how the existence of indefinite causality naturally emerges in higher-order quantum transformations and provide a simple strategy for the characterisation of mappings that have to preserve properties in a 'complete' sense, i.e., when acting non-trivially only on parts of an input space.