Causal Order Research Articles

Trading Causal Order for Locality.

Quantum theory admits ensembles of quantum nonlocality without entanglement (QNLWE). These ensembles consist of seemingly classical states (they are perfectly distinguishable and nonentangled) that cannot be perfectly discriminated with local operations and classical communication (LOCC). Here, we analyze QNLWE from a causal perspective, and show how to perfectly discriminate some of these ensembles using local operations and classical communication without definite causal order. Specifically, three parties with access to an instance of indefinite causal order-the Araújo-Feix-Baumeler-Wolf process-can perfectly discriminate the states in a QNLWE ensemble-the SHIFT ensemble-with local operations. Hence, this type of quantum nonlocality disappears at the expense of definite causal order while retaining classical communication. Our results thereby leverage the fact that LOCC is a conjunction of three constraints: local operations, classical communication, and definite causal order. Moreover, we show how multipartite generalizations of the Araújo-Feix-Baumeler-Wolf process are transformed into multiqubit ensembles that exhibit QNLWE. Such ensembles are of independent interest for cryptographic protocols and for the study of separable quantum operations unachievable with LOCC.

NARRATIVE STRUCTURE ANALYSIS OF PALM TREE MYTH IN PEDAWA VILLAGE, BANJAR DISTRICT, BULELENG REGENCY

Pedawa Village, located in Banjar District, Buleleng Regency, owns a myth about the palm tree growth in the village, which was told from one generation to another. The myth becomes guidelines for the villagers which is still applied at the present day. The purpose of the research is to break down the myth based on its narrative structure forms. To break down the myth the theory of narrative structure from Roland Barthes and Alan Dundes is used. Through the analysis process, it is revealed that this myth consisted of main function and catalytic function. Aside from that, the myth is also consisted of motifeme structure. The two breaking down processes proved that the myth fulfills the temporal and causality order.
 Keyword: myth, Pedawa, palm tree, narrative, structure

Curiosity Causes Creativity? Revealing the Reinforcement Circle between State Curiosity and Creativity

ABSTRACTCuriosity has long been extolled as a seed for employee creativity. This causality is plausible when considering curiosity as a stable trait. However, curiosity can also oscillate as a transitory state, thus complicating the causal sequence between such state curiosity and creativity. To clarify the causal ordering and achieve a refined understanding of what role state curiosity plays in the creative process, we utilize a random‐intercept cross‐lagged panel model (RI‐CLPM) to estimate the reciprocal effects between state curiosity and creativity at the within‐person level. With a full longitudinal panel data across three‐time points (N = 400), we find that at the within‐person level, state curiosity has a positive lagged effect on creativity and creativity also shows a positive lagged effect on state curiosity. This research highlights that curiosity and creativity, in fact, mutually reinforce each other.

The SWITCH test for discriminating quantum evolutions

We study different quantum circuits that can discriminate between two arbitrary quantum evolution operators. These circuits can be used to check whether two quantum operators are equal or not and to estimate a fidelity measure telling how close the operators are. This operator comparison is related to the SWAP test for discriminating two quantum states. In terms of their practical realization, we comment possible laboratory implementations with light along the same lines of recent experimental realizations of quantum superpositions of causal orders exploiting the different degrees of freedom of photons. We also discuss hardware efficient realizations for noisy intermediate scale quantum computers. Finally, we comment potential applications to the discrimination of quantum communication channels and to the search for simpler quantum circuits in quantum compilers.

Toward the Advantages of Quantum Trajectories on Entanglement Distribution in Quantum Networks

Quantum mechanics allows an information carrier to traverse through multiple trajectories of communication channels simultaneously, this leads us to quantum trajectories where alternative causal orders of communication channels being traversed are in a superposition, so that the relative orders of communication channels become indefinite. It has been shown that entanglement distribution process over quantum trajectory with an EPR pair being prepared by a sender enables quantum teleportation process to be heralded as a noiseless communication process with probability, the heralded result of which is unachievable in classical trajectory with a definite causal order of communication channels. In this work, we investigate the potential advantage of quantum trajectory on the generation of link-level entanglement, the basic element of quantum networks. To this aim, the performance of entanglement distribution process over multiple quantum trajectories where an EPR pair is prepared by a communication provider is analyzed. Besides, the application of entanglement purification protocol on quantum trajectory is first analyzed to reduce the effect of noise on link-level entanglement. The analysis shows that the link-level entanglement generated over quantum trajectory is more robust to long-distance quantum communication, with respect to classical trajectory, which paves the way for designing effective quantum networks.

STGC-GNNs: A GNN-based traffic prediction framework with a spatial–temporal Granger causality graph

Accurate representation of the temporal dynamics of traffic flow traveling in the road network is the key to traffic prediction, it is therefore important to model the spatial dependence of the road network. The essence of spatial dependence is to accurately describe how traffic information transmission is affected by other nodes in the road network, and the GNN-based traffic prediction model, as a benchmark for traffic prediction, has become the most common method for the ability to model spatial dependence by transmitting traffic information with the message passing mechanism. However, existing methods model a local and static spatial dependence, which cannot transmit the global-dynamic traffic information (GDTi) required for long-term prediction. The challenge is the difficulty of detecting the precise transmission of GDTi due to the uncertainty of individual transport, especially for long-term transmission. In this paper, we propose a new hypothesis: GDTi behaves macroscopically as a transmitting causal relationship (TCR) underlying traffic flow, which remains stable under dynamic changing traffic flow. We further propose spatial–temporal Granger causality (STGC) to express TCR, which models global and dynamic spatial dependence. To model global transmission, we model the causal order and causal lag of TCR’s global transmission by a spatial–temporal alignment algorithm. To capture dynamic spatial dependence, we approximate the stable TCR underlying dynamic traffic flow by a Granger causality test. The experimental results on three backbone models show that using STGC to model the spatial dependence has better results than the original model for 45 min and 1 h long-term prediction.

Heterogeneous interventional effects with multiple mediators: Semiparametric and nonparametric approaches

Abstract We propose semiparametric and nonparametric methods to estimate conditional interventional indirect effects in the setting of two discrete mediators whose causal ordering is unknown. Average interventional indirect effects have been shown to decompose an average treatment effect into a direct effect and interventional indirect effects that quantify effects of hypothetical interventions on mediator distributions. Yet these effects may be heterogeneous across the covariate distribution. We consider the problem of estimating these effects at particular points. We propose an influence function-based estimator of the projection of the conditional effects onto a working model, and show under some conditions that we can achieve root-n consistent and asymptotically normal estimates. Second, we propose a fully nonparametric approach to estimation and show the conditions where this approach can achieve oracle rates of convergence. Finally, we propose a sensitivity analysis that identifies bounds on both the average and conditional effects in the presence of mediator-outcome confounding. We show that the same methods easily extend to allow estimation of these bounds. We conclude by examining heterogeneous effects with respect to the effect of COVID-19 vaccinations on depression during February 2021.

Sequential device-independent certification of indefinite causal order

Sequential device-independent certification of indefinite causal order

Causal Order Complex and Magnitude Homotopy Type of Metric Spaces

Abstract In this paper, we construct a pointed CW complex called the magnitude homotopy type for a given metric space $X$ and a real parameter $\ell \geq 0$. This space is roughly consisting of all paths of length $\ell $ and has the reduced homology group that is isomorphic to the magnitude homology group of $X$. To construct the magnitude homotopy type, we consider the poset structure on the spacetime $X\times \mathbb{R}$ defined by causal (time- or light-like) relations. The magnitude homotopy type is defined as the quotient of the order complex of an intervals on $X\times \mathbb{R}$ by a certain subcomplex. The magnitude homotopy type gives a covariant functor from the category of metric spaces with $1$-Lipschitz maps to the category of pointed topological spaces. The magnitude homotopy type also has a “path integral” like expression for certain metric spaces. By applying discrete Morse theory to the magnitude homotopy type, we obtain a new proof of the Mayer–Vietoris-type theorem and several new results including the invariance of the magnitude under sycamore twist of finite metric spaces.

Dynamic Relationships among Composite Property Prices of Major Chinese Cities: Contemporaneous Causality through Vector Error Corrections and Directed Acyclic Graphs

The present study is the first one that investigates dynamic relations among composite real estate price indices of ten different cities in China during the years from 2005 to 2021. Utilizing the data recorded on a monthly basis, we apply VECM (vector error-correction modeling) and DAGs (directed acyclic graphs) in order to characterize contemporaneous causal relations among the ten real estate price indices. We use the PC algorithm to identify a pattern with non-directed edges and the LiNGAM algorithm to determine the causal ordering, based on which we calculate the results of innovation accounting. The LiNGAM algorithm adopted here effectively utilizes non-normality for facilitating the arrival of complete causal orderings. Our results show that price dynamics revealed through processes of price adjustments due to shocks to prices are rather sophisticated and such dynamics are, in general, dominated by price indices of Shanghai and Shenzhen, which are two top-tier cities among the four top-tier cities in China. This indicates that policy design on composite property prices should be focusing on price indices of Shanghai and Shenzhen.

Novel Ordering-Based Approaches for Causal Structure Learning in the Presence of Unobserved Variables

We propose ordering-based approaches for learning the maximal ancestral graph (MAG) of a structural equation model (SEM) up to its Markov equivalence class (MEC) in the presence of unobserved variables. Existing ordering-based methods in the literature recover a graph through learning a causal order (c-order). We advocate for a novel order called removable order (r-order) as they are advantageous over c-orders for structure learning. This is because r-orders are the minimizers of an appropriately defined optimization problem that could be either solved exactly (using a reinforcement learning approach) or approximately (using a hill-climbing search). Moreover, the r-orders (unlike c-orders) are invariant among all the graphs in a MEC and include c-orders as a subset. Given that set of r-orders is often significantly larger than the set of c-orders, it is easier for the optimization problem to find an r-order instead of a c-order. We evaluate the performance and the scalability of our proposed approaches on both real-world and randomly generated networks.

The causality-chance binomial: explanatory principle of scientific knowledge in medicine?

This essay questions mathematical truths as an explanatory principle of the medical scientific knowledge. It analyzes, in the first place, the current concept of normality based on a distribution of probabilistic values and its limitations and mistakes to capture the complexity of the human condition are highlighted. The closed systems (gambling) origin of the theory of probabilities and the binomial causality-chance are compared with open systems typical of the complexity of the vital process, and their extreme differences are argued. The nonsense of depositing in the causality-chance binomial the meaning of associations between events typical of the complexity of human life in health and disease is highlighted. The characteristics of mechanistic causality (punctual, homogeneous, linear, unidirectional and fixed), which equates the organism with a machine and is the only accepted scientific explanation of events of human life, are confronted with those of contextual causality (diffuse, heterogeneous, hierarchical, multidirectional and changing), which specifies various interacting causal orders that shape of human condition: the historical, the social, the political, the economical, the cultural or the biological that represents a scrutinizing and penetrating look at the complexity of human beings. It concludes the superiority of contextual causality over mechanistic causality that opens up explanatory possibilities of the vital events that are usually put away as "effects of chance". This integrative approach to the human complexity can enrich and strengthen the clinical method that is now degraded and at risk of extinction.

Reassessing thermodynamic advantage from indefinite causal order

Indefinite causal order is a key feature involved in the study of quantum higher order transformations. Recently, intense research has been focused on possible advantages related to the lack of definite causal order of quantum processes. Quite often the quantum switch is claimed to provide advantages in information-theoretic and thermodynamic tasks. We address here the question whether indefinite causal order is a resource for quantum thermodynamics. Inspired by previous results in the literature, we show that indefinite causal order is not necessary for the reported increase in free energy and ergotropy. More specifically, we show that a simple causally ordered process, which replaces the system's state with a new one before the final measurement, outperforms the quantum switch in all thermodynamic tasks considered so far. We further show that a similar advantage can be also achieved without completely discarding system, if we allow for non-Markovian interactions between the system and an environment. We extend the analysis to more extreme examples of indefinite causal order, showing that they do not provide an advantage either. Finally, we discuss a possible way to study the advantages that may arise from indefinite causal order in a general scenario.

Hate prompts participation: Examining the dynamic relationship between affective polarization and political participation

Political participation has long been considered a cornerstone of democracy. While most studies on political participation have been grounded on a normative approach, which assumes that political participation is driven by positive civic actions, recent studies suggest that political participation is also driven by negative forces such as overconfidence in knowledge and partisan communication flows. This article builds on the latter line of research by exploring the role of affective polarization on political participation. While this line of research is not new, it suffers from a lack of clear causal order. This study relies on three-wave panel data collected during the 2022 Korean presidential election. To ensure methodological rigor, we utilized both cross-lagged and fixed-effects panel analyses. Our findings show that affective polarization stimulates political participation rather than the reverse path. In addition, we did not observe a reciprocal relationship. Implications for democracy are discussed.

Causal structure in the presence of sectorial constraints, with application to the quantum switch

Existing work on quantum causal structure assumes that one can perform arbitrary operations on the systems of interest. But this condition is often not met. Here, we extend the framework for quantum causal modelling to situations where a system can suffer sectorial constraints, that is, restrictions on the orthogonal subspaces of its Hilbert space that may be mapped to one another. Our framework (a) proves that a number of different intuitions about causal relations turn out to be equivalent; (b) shows that quantum causal structures in the presence of sectorial constraints can be represented with a directed graph; and (c) defines a fine-graining of the causal structure in which the individual sectors of a system bear causal relations. As an example, we apply our framework to purported photonic implementations of the quantum switch to show that while their coarse-grained causal structure is cyclic, their fine-grained causal structure is acyclic. We therefore conclude that these experiments realize indefinite causal order only in a weak sense. Notably, this is the first argument to this effect that is not rooted in the assumption that the causal relata must be localized in spacetime.

Indefinite causality in quantum mechanics and its thermodynamic applications

The nature of causality remains one of the key puzzles in science. In quantum theory, the causal structure is not subject to quantum uncertainty and plays rather a background role. One can ask whether the background causal structure can be dropped, for example, by respecting causality only locally. Such scenarios of local validity of quantum theory while relaxing the global definite causal order of operations can be described via the machinery of process matrices. An important example of scenarios of this kind is quantum SWITCH, a process realizing a quantum superposition of causal orders of operations. Looking for the possible applications of quantum SWITCH has been the subject of growing interest in the scientific community as it could provide communication and computational resources not realizable via standard quantum theory. In the last few years, the benefits potentially offered by quantum SWITCH for thermodynamic tasks have appeared in the spotlight. This contribution aims at reviewing the recent proposals of thermodynamic applications of quantum SWITCH and draw the perspectives.

Quantumness of qubit states interacting with two structured reservoirs in indefinite causal order

Quantumness is studied for single and bipartite states of qubits which interact with the identical structured reservoirs in indefinite causal order. Coherence and temporal steerability are investigated for a single-qubit state. Entanglement, spatial steerability and the Bell-CHSH nonlocality are examined for a bipartite-qubit state. It is shown that the indefinite causal order can reduce the decay of the quantumness of qubit states.

Measuring Evidence for Mediation in the Presence of Measurement Error

Mediation analysis empirically investigates the process underlying the effect of an experimental manipulation on a dependent variable of interest. In the simplest mediation setting, the experimental treatment can affect the dependent variable through the mediator (indirect effect) and/or directly (direct effect). However, what appears to be an indirect effect in standard mediation analysis may reflect a data-generating process without mediation, including the possibility of a reversed causal ordering of measured variables, regardless of the statistical properties of the estimate. To overcome this indeterminacy where possible, the authors develop the insight that a statistically reliable total effect, combined with strong evidence for conditional independence of the treatment and the outcome given the mediator, is unequivocal evidence for mediation as the underlying causal model into an operational procedure. This is particularly helpful when theory is insufficient to definitely causally order measured variables, or when the dependent variable is measured before what is believed to be the mediator. The procedure combines Bayes factors as principled measures of the degree of support for conditional independence, with latent variable modeling to account for measurement error and discretization in a fully Bayesian framework. The authors reanalyze a set of published mediation studies to illustrate how their approach facilitates stronger conclusions.

Indefinite Causal Order for Quantum Phase Estimation with Pauli Noise

This paper further explores the recent scheme of switched quantum channels with indefinite causal order applied to the reference metrological task of quantum phase estimation in the presence of noise. We especially extend the explorations, previously reported with depolarizing noise and thermal noise, to the class of Pauli noises, important to the qubit and not previously addressed. Nonstandard capabilities, not accessible with standard quantum phase estimation, are exhibited and analyzed, with significant properties that are specific to the Pauli noises, while other properties are found in common with the depolarizing noise or the thermal noise. The results show that the presence and the type of quantum noise are both crucial to the determination of the nonstandard capabilities from the switched channel with indefinite causal order, with a constructive action of noise reminiscent of stochastic resonance phenomena. The study contributes to a more comprehensive and systematic characterization of the roles and specificities of quantum noise in the operation of the novel devices of switched quantum channels with indefinite causal order.

Leggett-Garg test of macrorealism using indefinite causal order of measurements

Macrorealism is a belief that constitutes the core of our perception of reality in the everyday world. The Leggett-Garg (LG) test is a conceptually elegant approach for probing the compatibility between the notion of macrorealism and quantum theory. However, a conclusive LG test hinges on how one fixes the operational invasiveness loophole, i.e., how the statistical form of non-invasive measurability assumption is guaranteed in an LG test. Despite many attempts to close this loophole, no consensus has been achieved yet. In this work, we propose a simple and elegant scheme based on indefinite causal order in quantum switch experiment, which enables us to close this loophole, and eventually, the LG test becomes a conclusive test of macrorealism.