Causal Scenarios Research Articles

Precision-based causal inference modulates audiovisual temporal recalibration.

Cross-modal temporal recalibration guarantees stable temporal perception across everchanging environments. Yet, the mechanisms of cross-modal temporal recalibration remain unknown. Here, we conducted an experiment to measure how participants' temporal perception was affected by exposure to audiovisual stimuli with consistent temporal delays. Consistent with previous findings, recalibration effects plateaued with increasing audiovisual asynchrony and varied by which modality led during the exposure phase. We compared six observer models that differed in how they update the audiovisual temporal bias during the exposure phase and whether they assume modality-specific or modality-independent precision of arrival latency. The causal-inference observer shifts the audiovisual temporal bias to compensate for perceived asynchrony, which is inferred by considering two causal scenarios: when the audiovisual stimuli have a common cause or separate causes. The asynchrony-contingent observer updates the bias to achieve simultaneity of auditory and visual measurements, modulating the update rate by the likelihood of the audiovisual stimuli originating from a simultaneous event. In the asynchrony-correction model, the observer first assesses whether the sensory measurement is asynchronous; if so, she adjusts the bias proportionally to the magnitude of the measured asynchrony. Each model was paired with either modality-specific or modality-independent precision of arrival latency. A Bayesian model comparison revealed that both the causal-inference process and modality-specific precision in arrival latency are required to capture the nonlinearity and asymmetry observed in audiovisual temporal recalibration. Our findings support the hypothesis that audiovisual temporal recalibration relies on the same causal-inference processes that govern cross-modal perception.

Towards generative digital twins in biomedical research

Digital twins in biomedical research, i.e. virtual replicas of biological entities such as cells, organs, or entire organisms, hold great potential to advance personalized healthcare. As all biological processes happen in space, there is a growing interest in modeling biological entities within their native context. Leveraging generative artificial intelligence (AI) and high-volume biomedical data profiled with spatial technologies, researchers can recreate spatially-resolved digital representations of a physical entity with high fidelity. In application to biomedical fields such as computational pathology, oncology, and cardiology, these generative digital twins (GDT) thus enable compelling in silico modeling for simulated interventions, facilitating the exploration of ‘what if’ causal scenarios for clinical diagnostics and treatments tailored to individual patients. Here, we outline recent advancements in this novel field and discuss the challenges and future research directions.

A systems theory-based safety assessment of pre-treatment patient-specific quality assurance for intensity-modulated treatments in a single-vendor environment

Background and purposeWhile patient-specific quality assurance (PSQA) has been integral to intensity-modulated treatments, its value is debated. A systems approach to safety is essential for understanding complex systems like radiation oncology but is often overlooked in PSQA research. This study aims to elucidate PSQA’s fundamental value and identify opportunities for enhancing safety in intensity-modulated treatments. Materials and MethodsFirst, causal scenarios that could lead to patient harm were identified using a prospective safety assessment technique developed for complex systems. Second, PSQA’s ability to mitigate these scenarios was evaluated using standard stability and control principles. The analysis also included safeguards related to PSQA, such as daily linac QA, equipment commissioning, and equipment design. ResultsTen causal scenarios were identified, highlighting well-known issues like flawed algorithms, data corruption, and hardware errors. Mitigation is achieved through advanced dose calculation and optimization algorithms, software and data integration, and preconfigured beam data, which improve decision-making and system state determination. Modern linac control systems enhance all aspects of system stability and control. Commissioning, daily linac QA, and PSQA are effective in enhancing the determination of system states only when feedback is non-overlapping and unambiguous. ConclusionGiven equipment improvement and related safeguards, the feedback generated from PSQA has diminished in value. To better complement other safeguards, PSQA should evolve to provide automated, unambiguous detection of any potential catastrophic treatment deviations prior to treatment. This evolution would allow physicists to focus on more critical aspects of patient care in radiation oncology.

Integrating security in hazard analysis using STPA-Sec and GSPN: A case study of automatic emergency braking system

Hazard analysis is a vital step in developing intelligent connected vehicles, aiming to eliminate or control hazards in the initial stages of system development and to provide theoretical support for the system’s safety design. However, conventional hazard analysis methods, such as Fault Tree Analysis and Failure Mode and Effects Analysis, suffer from two shortcomings: they do not account for the impact of cybersecurity factors on system safety and do not provide sufficient quantification of hazard scenarios. To this end, we propose a quantifiable hazard analysis method with security consideration, which integrates System Theoretic Process Analysis for Security (STPA-Sec) and Generalized Stochastic Petri Net (GSPN), supporting the extraction, modeling, and quantification of hazards. Specifically, we employ STPA-Sec for qualitative analysis to identify causal scenarios, safety requirements, security requirements, and the corresponding mitigations. Then, based on the identified causal scenarios, a GSPN model is established to quantify system-level hazards. A case study on a real open-source test vehicle demonstrates that the proposed method not only offers a comprehensive analysis of hazards but also provides a quantitative assessment. Comparative assessments suggest that the proposed method exhibits an advantage in terms of analysis processes (integrating security) and results (quantification).

Identification of causal influences in quantum processes

Recent years have seen great interest in extending causal inference concepts developed in the context of classical statistical models to quantum theory. So far, this program has only barely addressed causal identification, a type of causal inference problem concerned with recovering from observational data and qualitative assumptions the causal relationships generating the data, and hence the effects of hypothetical interventions. A major obstacle to a theory of causal identification in the quantum setting is the question of what should play the role of “observational data,” as any means of extracting data at a locus will almost certainly disturb the system. One might think that quantum measurements are already too much like interventions, so that the problem of causal identification is trivialized. This is not the case: when we fix a limited class of quantum measurement instruments (namely, the class of all projective measurements) to play the role of “observations,” there exist scenarios for which causal identification is impossible. In this paper, we present a framework, based on process theories (also known as strict symmetric monoidal categories), for studying quantum causal identification scenarios on the same footing as their classical counterparts. Within this framework, we present sufficient conditions for quantum causal identification in networks with unobserved confounding systems, including quantum analogs of the well-known “back-door” and “front-door” criteria. These results arise from a type of causal model designed to facilitate the transfer of inference techniques from the classical to the quantum setting. Published by the American Physical Society 2024

Classifying causal structures: Ascertaining when classical correlations are constrained by inequalities

The classical causal relations between a set of variables, some observed and some latent, can induce both equality constraints (typically conditional independencies) as well as inequality constraints (Instrumental and Bell inequalities being prototypical examples) on their compatible distribution over the observed variables. Enumerating a causal structure's implied inequality constraints is generally far more difficult than enumerating its equalities. Furthermore, only inequality constraints ever admit violation by quantum correlations. For both those reasons, it is important to classify causal scenarios into those which impose inequality constraints versus those which do not. Here we develop methods for detecting such scenarios by appealing to d separation, e separation, and incompatible supports. Many (perhaps all?) scenarios with exclusively equality constraints can be detected via a condition articulated by Henson, Lal, and Pusey (HLP). Considering all scenarios with up to four observed variables, which number in the thousands, we are able to resolve all but three causal scenarios, providing evidence that the HLP condition is, in fact, exhaustive. Published by the American Physical Society 2024

Complying with ISO 26262 and ISO/SAE 21434: A Safety and Security Co-Analysis Method for Intelligent Connected Vehicle.

A cyber-physical system (CPS) integrates communication and automation technologies into the operational processes of physical systems. Nowadays, as a complex CPS, an intelligent connected vehicle (ICV) may be exposed to accidental functional failures and malicious attacks. Therefore, ensuring the ICV's safety and security is crucial. Traditional safety/security analysis methods, such as failure mode and effect analysis and attack tree analysis, cannot provide a comprehensive analysis for the interactions between the system components of the ICV. In this work, we merge system-theoretic process analysis (STPA) with the concept phase of ISO 26262 and ISO/SAE 21434. We focus on the interactions between components while analyzing the safety and security of ICVs to reduce redundant efforts and inconsistencies in determining safety and security requirements. To conquer STPA's abstraction in describing causal scenarios, we improved the physical component diagram of STPA-SafeSec by adding interface elements. In addition, we proposed the loss scenario tree to describe specific scenarios that lead to unsafe/unsecure control actions. After hazard/threat analysis, a unified risk assessment process is proposed to ensure consistency in assessment criteria and to streamline the process. A case study is implemented on the autonomous emergency braking system to demonstrate the validation of the proposed method.

Reprezentarea cunoștințelor în cursurile de instruire asistată de calculator

The article discusses the issues of knowledge representation in computer assisted instruction courses using causal scenarios - a type of frames. The concept of a stereotypical educational situation is introduced as the main constructive element of a causal scenario. The knowledge presented in causal scenarios can be used to organize learning in a student-computer dialogue mode.

Utility of Stochastic Ground Motion Models in Conditional Spectrum-Based Selection Consistent with the Causal Earthquakes

ABSTRACT Conditional Spectrum (CS), characterized by a conditional mean and conditional standard deviation, serves as a target spectrum linking seismic hazard information to ground motion selection for seismic demand analysis. Current Ground Motion Selection and Modification (GMSM) methods aim to align with this target spectrum but face challenges due to the limited availability of recorded ground motions and potential modifications, leading to difficulties in ensuring selected ground motions accurately reflect site characteristics. Addressing this issue, the study presents an algorithm to select hazard-consistent CS-based records utilizing a stochastic ground motion model with a dual objective: (1) alleviate the need for scaling during the selection process, and (2) select records consistent with the target hazard and the contributing causal scenarios. To achieve both objectives, the study generates a database of hazard-targeted ground motions utilizing the kriging surrogate with scenarios sampled from the disaggregation matrix. Then, a postprocessing step explicitly considers causative parameters in the selection process. The algorithm’s potential to select site-specific ground motions is demonstrated using sites in the Western United States, providing insights into the computational cost and accuracy. Additionally, statistical comparisons are conducted to explore circumstances where similar hazard consistency can be achieved without the postprocessing step, reducing the overall computational cost. Finally, the selected ground motions are favorably compared to those selected through traditional conditional spectrum-based approaches based on their spectral shape and ground motion intensity measures.

Comparing the cost of violating causal assumptions in Bell experiments: locality, free choice and arrow-of-time.

The causal modelling of Bell experiments relies on three fundamental assumptions: locality, freedom of choice and arrow-of-time. It turns out that nature violates Bell inequalities, which implies the failure of at least one of those assumptions. Since rejecting any of them, even partially, is sufficient to explain the observed correlations, it is natural to inquire about the cost in each case. This paper builds upon the findings in Blasiak et al. 2021 Proc. Natl Acad. Sci. USA 118, e2020569118 (doi:10.1073/pnas.2020569118) showing the equivalence between the locality and free choice assumptions. Here, we include retrocausal models to complete the picture of causal explanations of the observed correlations. Furthermore, we refine the discussion by considering more challenging causal scenarios which allow only single-arrow type violations of a given assumption. The figure of merit chosen for the comparison of the causal cost is defined as the minimal frequency of violation of the respective assumption required for a simulation of the observed experimental statistics. This article is part of the theme issue 'Quantum contextuality, causality and freedom of choice'.

A Regularity Theory of Causation

AbstractIn this paper, we propose a regularity theory of causation. The theory aims to be reductive and to align with our pre‐theoretic understanding of the causal relation. We show that our theory can account for a wide range of causal scenarios, including isomorphic scenarios, omissions, and scenarios which suggest that causation is not transitive.

A novel system-theoretic approach for human-system collaboration safety: Case studies on two degrees of autonomy for autonomous ships

Shore control center (SCC) operators of maritime autonomous surface ships (MASS) may be allocated with complex responsibilities within different degrees of autonomy (DoA), from remote control to remote supervision, and the role of human in-the-loop is significant. In the current research on MASS, however, the safety of interactions between humans and systems are not sufficiently considered. Hence, this paper proposes a system-theoretic approach to safety analysis for human-system collaboration. The novelty of the approach is the definition of operational contexts of MASS and the integration of a human cognitive model into the system theoretic process analysis (STPA), called STPA-Cog. The method is demonstrated in two case studies of MASS: (i) remote control mode (RCM) and (ii) remote supervision mode (RSM), focusing on two types of navigational accidents (i.e., collision and grounding). The analysis results are derived by comparing the human-related and technical-related causal scenarios in RCM and in RSM. The findings can assist in human-oriented design and operational planning for SCC of MASS. Further, the proposed approach also has the potential to be used and extended to systemic safety analysis in other intelligent transportation systems.

Estimation of time-varying causal effects with multivariable Mendelian randomization: some cautionary notes.

For many exposures present across the life course, the effect of the exposure may vary over time. Multivariable Mendelian randomization (MVMR) is an approach that can assess the effects of related risk factors using genetic variants as instrumental variables. Recently, MVMR has been used to estimate the effects of an exposure during distinct time periods. We investigated the behaviour of estimates from MVMR in a simulation study for different time-varying causal scenarios. We also performed an applied analysis to consider how MVMR estimates of body mass index on systolic blood pressure vary depending on the time periods considered. Estimates from MVMR in the simulation study were close to the true values when the outcome model was correctly specified: i.e. when the outcome was a discrete function of the exposure at the precise time points at which the exposure was measured. However, in more realistic cases, MVMR estimates were misleading. For example, in one scenario, MVMR estimates for early life were clearly negative despite the true causal effect being constant and positive. In the applied example, estimates were highly variable depending on the time period in which genetic associations with the exposure were estimated. The poor performance of MVMR to study time-varying causal effects can be attributed to model misspecification and violation of the exclusion restriction assumption. We would urge caution about quantitative conclusions from such analyses and even qualitative interpretations about the direction, or presence or absence, of a causal effect during a given time period.

Should Cognitive Screening Tests Be Corrected for Age and Education? Insights From a Causal Perspective.

Cognitive screening tests such as the Mini-Mental State Examination are widely used in clinical routine to predict cognitive impairment. The raw test scores are often corrected for age and education, although documented poorer discrimination performance of corrected scores has challenged this practice. Nonetheless, test correction persists, perhaps due to the seemingly counterintuitive nature of the underlying problem. We used a causal framework to inform the long-standing debate from a more intuitive angle. We illustrate and quantify the consequences of applying the age-education correction of cognitive tests on discrimination performance. In an effort to bridge theory and practical implementation, we computed differences in discrimination performance under plausible causal scenarios using Open Access Series of Imaging Studies (OASIS)-1 data. We show that when age and education are causal risk factors for cognitive impairment and independently also affect the test score, correcting test scores for age and education removes meaningful information, thereby diminishing discrimination performance.

Towards system-theoretic risk assessment for future ships: A framework for selecting Risk Control Options

While the concept of smart shipping is expected to shape the future of the maritime industry, its safety is still a major concern. New risks might emerge when shifting from human controllers onboard, to autonomous software controllers and remote human controllers. The uncertainties associated with the emerging risks require an efficient decision-making methodology to ensure ship safety. This paper proposes a framework for selecting Risk Control Options (RCOs) of ships with higher degrees of autonomy in the context of marine risk assessment and Formal Safety Assessment (FSA). The framework uses the System Theoretic Process Analysis (STPA) for the hazard analysis and the identification of RCOs, while Bayesian Network is employed in the framework for estimating the system risk. Integrating STPA and BN offers the possibility to cover most of the steps of both risk assessment and FSA and permits the prioritization of the identified RCOs. The proposed method is applied to a concept of an autonomous seawater cooling system (SWC) as an illustrative case study. The results indicate that the RCOs including sensors health monitoring and software testing should be prioritized to reduce the risk. This is unveiled by the STPA analysis which shows the risk contribution of the associated causal scenarios.

Navigating Representation: Reflections on the Futures of Kazakh Identity From a CLA Scenarios Process

This short essay reflects on the process of running a Causal Layered Analysis scenario exercise with the Institute for Professional Development at the Academy of Public Administration in Kazakhstan in collaboration with John Sweeney (from the then Qazaq Research Institute for Futures Studies). We worked with approximately 50 public administrators over a 3 day period, culminating in a Causal Layered Analysis scenario process that challenged and reviewed the national vision. The process surfaced fundamental dynamics and tensions in national identity and representation. The findings and process are detailed here.

A system-based operational assessment of external beam radiotherapy.

Advanced technologies have led to improvements in modern radiotherapy over the years. However, adoption of advanced technologies can present challenges to existing clinical operations and negatively impact safety. The purpose of this work is to perform an assessment of modern radiotherapy for the operational objectives of safety, efficiency, and financial viability. This work focuses on external beam radiotherapy (EBRT). The operational assessment included department management, treatment planning, treatment delivery, and associated workflows for three equipment configurations of Ethos, Halcyon, and TrueBeam with the ARIA information system, Eclipse treatment planning, and IDENTIFY surface guidance. Systems-theoretic process analysis (STPA) was used to analyze the related workflows. Control actions, unsafe contexts of those control actions, and associated causal scenarios that can lead to unsafe radiation and non-radiation physical injury (safety objective), reduced treatment capacity (efficiency objective), and costs that exceed budget (financial viability objective) were identified. The number of control actions (and causal scenarios) were 18 (254), 18 (267), and 20 (267) for the equipment configurations of Halcyon, TrueBeam, and Ethos, respectively. The extent that safety, efficiency, and financial viability were impacted is similar across the different equipment configurations, but there were some noteworthy differences related to information transfer and workflow bottlenecks potentially impacting access to care. Seventy-five percent of the scenarios across all three configurations were related to safety. Overall, 29% of the scenarios impacted more than one operational objective and 48% were related to human decisions during the process of care. Planned or unplanned process changes were responsible for 8% of the causal scenarios. Broad-based clinical improvements may be realized by addressing causal scenarios that impact multiple objectives. Redesigning the roles and responsibilities of the clinical team and some aspects of the radiotherapy workflow may be helpful to fully realize the benefits of advanced technologies. Radiotherapy may benefit from additional tools to improve the consistency between decisions and actions when system or process changes occur.

Chinese travellers’ mobility decision-making processes during public health crisis situations: a Bayesian network model

ABSTRACT Travellers’ mobility decisions are fraught with uncertainty and instability during public health crises. However, existing studies have not revealed the internal mechanism of travellers’ mobility changes in a public health crisis. This paper established and trained a Bayesian network model from multiple data to analyse Chinese travellers’ mobility decision-making processes under COVID-19 and simulated the changes in mobility decisions in different scenarios. The results show that travellers reformulate mobility decisions in response to various information and negotiate between social customs and personal needs. Mobility can be modified through risk communication and habits adaptation. Bayesian network models provide a methodological contribution to causal exploration and scenario prediction.

A comparative study of factors associated with motorcycle crash severities under different causal scenarios

This study was carried out to examine the factors associated with motorcycle crash severity in Alabama, under different manner of crash and causal scenarios using mixed logit modeling. Three crash mechanisms were considered in this study: single-vehicle motorcycle crash with motorcyclist at fault, multi-vehicle collision between a motorcycle and another vehicle with motorcyclist being at fault, and motorcyclist not at fault in a collision between a motorcycle and another vehicle. The model estimation results showed that crashes that happened in rural areas were more likely to be severe, irrespective of the causal unit or manner of collision. The results also show that fatigue among motorcyclists was associated with severe injury, whereas driver fatigue was linked to no injury outcome. Further, it was found that risky behaviors such as speeding, driving/riding under the influence of alcohol or drugs, driving/riding with invalid license were significantly associated with severe injury outcome. Developing the injury-severity models based on the segmented crash data has helped to reveal some similarities and differences in crash outcomes based on the crash mechanism and the at-fault road user. It is expected that these findings would provide a data-driven evidence to improve motorcycle safety in the state.

Story order in attribution of moral responsibility

AbstractDiscourse comprehension relies on the construction of a mental model that represents the unfolding in time of the events described. In causal scenarios, where the action of one agent (the enabler) temporally precedes and enables the action of another agent (the causer), discourse may reflect the underlying event structure by describing the enabler’s action first and then the causer’s action (story order) or may describe the causer’s action first (backward order). Studies in the literature have shown that adults consider causers to be more responsible than enablers in moral scenarios. Based on the assumption that story order favors the construction of a mental model of events, we conducted an experiment to test the prediction that preference for the causer over the enabler should be greater when events are presented in story order than in backward order. The participants in the experiment were 42 fifth-grade children, 42 adolescents, and 42 adults. The results of the experiment confirmed the prediction for all three groups of participants. We discuss the practical implications of these results for learning contexts, legal contexts, and the psychology of moral judgments.