Causal Analysis Framework Research Articles

The effect of urban resilience on social stability under external disaster strikes--Evidence from China during COVID-19 pandemic

The robustness of civil society plays a pivotal role in shaping the aftermath of disasters. Following the COVID-19 pandemic, the question arises: why did some cities witness widespread resident protests, while others remained relatively calm? Certain theoretical studies have suggested that urban economic disruptions stemming from external disasters can significantly undermine social stability, yet these studies lack empirical validation of the underlying mechanisms. This research endeavors to explore the factors within urban resilience that influence social stability during major public events, a topic that has received scant attention in existing empirical studies.Utilizing data from the Chinese General Social Survey (2021), this study introduces an empirical causal analysis framework integrating the "entropy value method" and "fuzzy-set Qualitative Comparative Analysis (fsQCA)" to investigate the following: (1) The univariate test reveals that the consistency level of all conditions is below 0.9, indicating that no single-dimensional factor alone can enhance social stability. (2) The absence of urban economic resilience emerges as a critical conditional variable contributing to non-high levels of social stability, thereby serving as the primary driver of social instability. (3) Group analysis identifies three distinct pathways through which urban resilience impacts social stability: the "economic-ecological-resource" support path, the "economic-ecological-risk" mitigation path, and the "economic-ecological-challenge" resolution path.

Green innovation ecosystem evolution: Diffusion of positive green innovation game strategies on complex networks

The essence of the evolution of the green innovation ecosystem is the process by which the positive strategy of green innovation spreads in a complex network. In this study, the "Object-Technology-Environment-Management-Culture (OETMC)" structure paradigm of innovation ecosystems is presented, and a causal analysis framework of green innovation ecosystem evolution based on green innovation behavior strategy, supported by green innovation efficiency and focused on green innovation ecosystems is developed. Based on this framework, the structure of the green innovation ecosystem and the diffusion process of positive green innovation in the network are discussed. The results reveal the following. (1) The complexity of the green innovation ecological network has visibly improved, but numerous blank connections remain. These are the key constraints that hinder structural evolution. (2) The appearance of a large number of long-range connections weakens the constraint of geographical distance but also hinders edge nodes from joining the network. (3) The existence of preference attachment and power law distribution of the green innovation ecological network leads to the incomplete convergence of positive green innovation strategy at any initial strategy ratio. (4) Default cost and pollution tax rate ensure the enthusiasm of green innovation actors to seek innovation cooperation by increasing opportunity costs, thus accelerating the formation and reconstruction of the green innovation ecological network. These conclusions provide a decision-making reference for governments to encourage green innovation subjects to formulate targeted policies.

A quantitative causal analysis and optimization framework for inclusions of steel products

Inclusions generated during steelmaking and continuous casting processes could seriously reduce the mechanical properties of steel products and greatly increase the risk of corrosion failure. Much attention has been paid to perceiving the origin and classification of inclusions based on the process knowledge and mechanism model, while the value of industrial big data is underestimated. Machine learning (ML) methods have demonstrated potential in the processing of nonlinear and strong coupling relationships in industrial data. However, existing ML models are frequently unable to analyze causal relationships between manufacturing process, which is essential for the quality optimization. To address the limitation, this paper proposes a quantitative causal analysis and optimization framework. This framework starts with a feature selection module using propensity score matching (PSM) to measure the quantitative and reliable causal relationship between the process parameters and inclusions. Subsequently, a high-performance AutoML method, AutoGluon-Tabular (AGT), is applied to quantify the optimization range and verify the optimization effect. Specifically, AGT is used to estimate the inclusion level in each window of the process parameter to be optimized, choose the best window of which to determine adjustment strategies, and eventually verify the change of inclusion levels after optimization. The comparative experiments based on the real data collected from a steel company demonstrate that the proposed framework reduces the inclusion rate by 8.85%, which is effective, efficient and has guiding significance for the inclusions control of steel products.

A framework for identifying factors controlling cyanobacterium Microcystis flos-aquae blooms by coupled CCM-ECCM Bayesian networks.

Cyanobacterial blooms in freshwater sources are a global concern, and gaining insight into their causes is crucial for effective resource management and control. In this study, we present a novel computational framework for the causal analysis of cyanobacterial harmful algal blooms (cyanoHABs) in Lake Kinneret. Our framework integrates Convergent Cross Mapping (CCM) and Extended CCM (ECCM) causal networks with Bayesian Network (BN) models. The constructed CCM-ECCM causal networks and BN models unveil significant interactions among factors influencing cyanoHAB formation. These interactions have been validated by domain experts and supported by evidence from peer-reviewed publications. Our findings suggest that Microcystis flos-aquae levels are influenced not only by community structure but also by ammonium, phosphate, oxygen, and temperature levels in the weeks preceding bloom occurrences. We demonstrated a non-parametric computational framework for causal analysis of a multivariate ecosystem. Our framework offers a more comprehensive understanding of the underlying mechanisms driving M. flos-aquae blooms in Lake Kinneret. It captures complex interactions and provides an explainable prediction model. By considering causal relationships, temporal dynamics, and joint probabilities of environmental factors, the proposed framework enhances our understanding of cyanoHABs in Lake Kinneret.

A MMEM-BN–Based Analyzing Framework for Causal Analysis of Ship Collisions

A MMEM-BN–Based Analyzing Framework for Causal Analysis of Ship Collisions

An empirical evaluation of approximate and exact regression-based causal mediation approaches for a binary outcome and a continuous or a binary mediator for case-control study designs

BackgroundIn the causal mediation analysis framework, several parametric regression-based approaches have been introduced in past years for decomposing the total effect of an exposure on a binary outcome into a direct effect and an indirect effect through a target mediator. In this context, a well-known strategy involves specifying a logistic model for the outcome and invoking the rare outcome assumption (ROA) to simplify estimation. Recently, exact estimators for natural direct and indirect effects have been introduced to circumvent the challenges prompted by the ROA. As for the approximate approaches relying on the ROA, these exact approaches cannot be used as is on case-control data where the sampling mechanism depends on the outcome.MethodsConsidering a continuous or a binary mediator, we empirically compare the approximate and exact approaches using simulated data under various case-control scenarios. An illustration of these approaches on case-control data is provided, where the natural mediation effects of long-term use of oral contraceptives on ovarian cancer, with lifetime number of ovulatory cycles as the mediator, are estimated.ResultsIn the simulations, we found few differences between the performances of the approximate and exact approaches when the outcome was rare, both marginally and conditionally on variables. However, the performance of the approximate approaches degraded as the prevalence of the outcome increased in at least one stratum of variables. Differences in behavior were also observed among the approximate approaches. In the data analysis, all studied approaches were in agreement with respect to the natural direct and indirect effects estimates.ConclusionsIn the case where a violation of the ROA applies or is expected, approximate mediation approaches should be avoided or used with caution, and exact estimators favored.

A hierarchical granger causality analysis framework based on information of redundancy for root cause diagnosis of process disturbances

The Granger causality (GC) test is a widely utilized method for diagnosing process disturbances’ root cause. However, its effectiveness is limited due to the challenge of handling variable redundancy, leading to potentially inaccurate results. To tackle this problem, this paper introduces a new redundancy detection technique incorporated into the GC test framework. The method introduces a sum-of-redundancy index, enabling the development of a variable-layering approach to identify the sequential impact of process variables during disturbances. Furthermore, a hierarchical analysis framework is established, within which GC tests are applied both within and between layers, effectively revealing the propagation paths of process disturbances. Case studies conducted on a benchmark simulation process demonstrate the enhanced accuracy and feasibility of the proposed framework.

VISPUR: Visual Aids for Identifying and Interpreting Spurious Associations in Data-Driven Decisions.

Big data and machine learning tools have jointly empowered humans in making data-driven decisions. However, many of them capture empirical associations that might be spurious due to confounding factors and subgroup heterogeneity. The famous Simpson's paradox is such a phenomenon where aggregated and subgroup-level associations contradict with each other, causing cognitive confusions and difficulty in making adequate interpretations and decisions. Existing tools provide little insights for humans to locate, reason about, and prevent pitfalls of spurious association in practice. We propose VISPUR, a visual analytic system that provides a causal analysis framework and a human-centric workflow for tackling spurious associations. These include a CONFOUNDER DASHBOARD, which can automatically identify possible confounding factors, and a SUBGROUP VIEWER, which allows for the visualization and comparison of diverse subgroup patterns that likely or potentially result in a misinterpretation of causality. Additionally, weproposea REASONING STORYBOARD, which uses a flow-based approach to illustrate paradoxical phenomena, as well as an interactive DECISION DIAGNOSIS panel that helps ensure accountable decision-making. Through an expert interview and a controlled user experiment, our qualitative and quantitative results demonstrate that the proposed "de-paradox" workflow and the designed visual analytic system are effective in helping human users to identify and understand spurious associations, as well as to make accountable causal decisions.

Nonlinear Dynamic Granger Causality Analysis Framework for Root-Cause Diagnosis of Quality-Related Faults in Manufacturing Processes

Root-cause diagnosis of quality-related faults plays a crucial role in ensuring stable product quality and high-efficient production for modern manufacturing processes. However, there exists complex nonlinear and dynamic sequential characteristics for process and quality data before and after faults, which contain important fault information. How to fully explore and use this information to locate the root-cause and identify the propagation path is a hot topic. Thus, a new nonlinear dynamic Granger causality analysis method is developed for root-cause diagnosis, which will provide timely and useful reference information to take reasonable measures for field engineers. First, an optimal variable division based on minimal redundancy maximal relevance algorithm is presented to get the quality-related variables. Then, a new attention-based random disturbance gated recurrent unit is designed for nonlinear dynamic Granger causality analysis, aiming at locating the root-cause and identifying the propagation path of quality-related faults. A typical manufacturing process, the hot strip mill process is used for verification. The results show the practicability of the proposed method, and its strong robustness to noise. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Reasonable and feasible root-cause diagnosis technology will provide timely reference information for field operators to take maintenance measures. However, the nonlinear and dynamic characteristics of time-series data before and after quality-related faults have brought great challenges to the traditional root-cause diagnosis techniques. To address this issue, a practical nonlinear dynamic Granger causality analysis method is proposed, which is purely data driven without knowing complex mechanism knowledge. It will provide feasible solutions for safety monitoring of hot strip mill process as the representative of manufacturing processes.

A mixed framework for causal impact analysis under confounding and selection biases: a focus on Egra dataset

A mixed framework for causal impact analysis under confounding and selection biases: a focus on Egra dataset

Counterfactual reasoning in space and time: Integrating graphical causal models in computational movement analysis

AbstractMovement analysis is distinguished by an emphasis on understanding via observation and association. However, an important component of movement from the human and computer modeling perspective is the processes that bring about movement behavior in the first place. This article contextualizes the graphical causal modeling framework (for association, intervention, and counterfactual causal analysis) in GIScience, and more specifically within movement analysis studies. This is done by modeling the movement behavior of football players, applied to spatiotemporal data generated by an agent‐based simulation. The movement dataset is thoroughly analyzed to infer the statistical associations among its variables, to estimate the effect of an intervention on some of those variables, and to answer a few counterfactual questions from the observations. We conclude that causal graphs (i.e., directed acyclic graphs), if implemented correctly, can assist analysts in infering causal relations from movement data. This research suggests the integration of causal graphs and agent‐based paradigms as one solution for computational movement analysis.

Dynamic causal modeling for nonstationary industrial process performance degradation analysis and fault prognosis

Performance degradation analysis and fault prognosis of industrial processes refer to distinguish different degradation stage and raise warnings before a failure occurs, which are crucial to ensure the operation efficiency and safety. However, processes usually have nonstationarity caused by operating condition switching, which may be confused with the underlying temporal variations in the degradation process. Considering that the degradation process is essentially the changes in short-term temporal information transmission between variables, we propose a dynamic causal graph-driven performance degradation analysis and fault prognosis framework (DCG-PDA), which can reveal the intrinsic connection between causality and degradation mechanism. First, a dynamic causal graph framework is constructed, in which the nonstationarity is removed by transfer entropy-guided symbolization, to extract significant short-term causal information transmission. Second, changes in causality are effectively captured and quantified, and the causal graphs corresponding to some time points with larger changes in causality are selected to characterize the representative causal patterns in the degradation process. Finally, a node anomaly score is designed to consider the accumulation of causal structural changes at anomaly time points and measure variable-wise degradation contributions, so as to realize accurate locations for key variables in the degradation process. The validity of the proposed method is illustrated through a real compressor fouling process.

Identity frame at metaphor layer as the driving force for nuclear strategy: The US nuclear strategy during the cold war

The formation of a nation’s nuclear strategy is understood from a typical structuralist perspective of security strategy studies and international politics. This makes it difficult to not only derive new strategic alternatives but also anticipate what nuclear strategy patterns can be developed in the future. This study aims to deepen our understanding of nuclear strategy formation through causal layered analysis and to present new strategic alternatives to existing discourse. Therefore, this study examines the driving force that influences nuclear strategy formation and changes in implicit cognitive layers. The cognitive variables of identity are derived, and the causal layered analysis framework is used to explain how identity factors give meaning to concepts regarding national security and how the concepts are linked to strategic initiatives. Furthermore, this study assumes that nuclear strategy arises from interactions among the key frame variables in the cognitive layers. The US nuclear strategy cases are examined using this theoretical framework to understand how their interactions have shaped security concepts and been reflected in the nuclear strategy discourse. This study derives propositions that the ingroup definition of identity frames affects nuclear strategy as a driving force and that interpreting the explicit layers’ realities by the implicit frames affects the formation of security concepts and the direction of nuclear strategy.

Warming and greening exacerbate the propagation risk from meteorological to soil moisture drought

Quantifying the propagation time (PT) and trigger threshold (TR) from meteorological to soil moisture drought is critically important for drought early warning and precise defense. Nevertheless, existing propagation characteristics usually have a low temporal-spatial resolution, and their dynamics and related driving mechanisms (such as global warming and greening) are still incompletely understood. To fill the knowledge gap, this study proposes a drought propagation model based on the Bayesian causal analysis framework for quantifying the PT and TR with a high resolution. Taking Northeast China (NEC) as a case study, we further explore the dynamics of drought propagation characteristics in recent decades and possible driving mechanisms using the sliding window and Random Forest model. Results showed that: (1) the drought PT varies spatially and temporally in the study area, with long PT in the central plain and western high-altitude areas in the early growing season (typically over 200 days), while short in the middle and late growing season in most regions (less than one month in July and August), The TR is generally lower than 80 mm in the western regions and do not change significantly with time; (2) the PT and TR in the vast central and western regions exhibit a downward trend in the late growing season, resulting in a strikingly increased risk of drought propagation; (3) Increasing vapor pressure deficit (VPD) due to warming, along with decreasing aridity index (AI) due to precipitation shortage are the main drivers on the accelerated drought propagation. Moreover, local greening has also played a critical role in accelerating propagation via transpiration that consumes soil water, which contributes more than 20% to propagation dynamics. Overall, this study sheds new insights into drought propagation dynamics and mechanisms in a changing environment, providing a promising avenue for drought early warning and mitigation.

Cause analysis framework from a safety capability perspective: Application to Tianjiayi hazardous chemical explosion accident

AbstractIdentifying the causes of accidents is essential for learning lessons and preventing similar accidents. Safety capability factors cannot be ignored when considering the causes of accidents. Therefore, safety capability is an important perspective to develop a cause analysis framework. This study focuses on a cause analysis framework based on safety capability in the 24Model. First, this study introduces the safety capability in the 24Model and elaborates on the five safety capability dimensions. Second, a 24Model‐based causal analysis framework from a safety capability perspective and its application process is proposed. Third, the Tianjiayi explosion accident is analyzed using the proposed framework. In this accident, fourteen safety capability factors in four categories: safety knowledge, safety awareness, safety habits, and safety psychology are identified and analyzed. The case study provides a critical process demonstration for analyzing safety capability factors and validates the usability of the proposed framework. Additionally, the strengths of the framework and the limitations of the study are discussed. In the future, this framework can facilitate the development of a safety capability factor database and data‐driven safety management approaches.

Threats and the conservative shift in public opinion: comparative causal mediation analyses from Turkey

ABSTRACT How do mortality reminders and aroused mortality fear affect people’s conservatism? Threats-politics studies assume that life-threatening dangers, fear of death, and conservatism go together, overlooking the distinction between threat exposure and mortality fear arousal. Further, these studies commonly fixate on the conventional threat of terrorism and predominantly employ samples from Western countries. Complementing past research, I use three survey experiments in Turkey to investigate the relationship between a broad array of threats (terrorism, health risks, moral hazards, and daily thoughts of death’s inevitability), mortality fear induced by the threats, and self-rated conservative orientation in a causal mediation analysis framework, using the terror management theory and non-representative Facebook samples (2018, 2020). The results reveal that the respondents receiving threatening stimuli (particularly terrorism mortality reminder) become less conservative as the mortality fear elevates. The length of mortality priming and the broader study context influence this indirect effect’s significance and replicability.

Nonlinear time-series analysis on the causality response of phytoplankton in a tributary of the Three Gorges Reservoir

水华的频发已成为当前三峡水库最为突出的生态环境问题之一。尽管水动力调控叠加上温度变暖和营养负荷增加会诱导水华暴发强度和频率增加,但仍缺乏有效的方法框架去利用野外观测数据评估环境因子与浮游植物间的因果关联。本研究以三峡水库澎溪河监测数据为例,采用非线性时序分析的建模框架来量化浮游植物的因果响应规律。数据来自于2007年6月至2018年9月澎溪河流域的高阳平湖和汉丰湖两个观测点,其中包含了水文、气象和水质及叶绿素a等11种变量。首先,利用奇异谱分析(SSA)分离了叶绿素a和环境因子的低维确定性动力学信号;其次,采用收敛交叉映射(CCM)方法检验了叶绿素a与环境因子间的因果关联。结果显示:① 气象因子、支流流量、水温、三峡大坝水位和上游调节坝水位是影响高阳平湖叶绿素a时序变化的重要因素;② 总氮、总磷为代表的营养盐只在汉丰湖观测点中表现出与叶绿素a的因果关系,且总氮较之于总磷对叶绿素a变化影响更为显著;③ CCM结果与传统的皮尔森相关性分析及格兰杰因果检验比较,证实非线性时序分析方法在分析浮游植物的因果响应上更具优势。本研究为水生态系统的因果建模提供了研究范例,也为推动利用长期观测数据评估三峡水库水华驱动因子提供了新的研究角度。;Harmful algae blooms in Three Gorges Reservoir (TGR) have become the most major environmental and ecological problem. Despite broad recognition that hydrological manipulation, warming climate and nutrient loading promote the intensity and frequency of bloom events, the main problem is still no science-based framework for evaluating the causal relationships between environmental factors and phytoplankton biomass by using field observation data. Thus, as a case study, a nonlinear time-series causality analysis framework was used to reveal the phytoplankton response in the TGR, China. Two sampling stations, named Lake Gaoyangping and Lake Hanfeng, were used to reconstruct the dynamics of real-world environmental systems with eleven parameters from June 2007 to September 2018. Singular spectrum analysis isolated low-dimensional, deterministic signals for chlorophyll-a(Chl.a) dynamics and other candidate drivers. Causal analysis with convergent cross-mapping (CCM) provided strong evidence that temperature, sunshine hours, precipitation, discharge in the tributary, and water level of TGR systematically influenced phytoplankton biomass dynamics in Lake Gaoyangping. In contrast, total nitrogen (TN) and total phosphorus (TP) showed a causal relationship with Chl.a only in Lake Hanfeng, and the effect of TN on Chl.a was more significant than that of TP. Finally, the research compares the nonlinear CCM analysis with the linear Pearson correlation analysis and Granger causality test. It confirms that the nonlinear causality method has more advantage in causal discovery based on long time-series monitoring data. Overall, this study provides a case for causal modelling of water ecosystems and a new research perspective for evaluating the environmental drivers of harmful algae blooms in TGR by using long-term observational data.

An individualized causal framework for learning intercellular communication networks that define microenvironments of individual tumors.

Cells within a tumor microenvironment (TME) dynamically communicate and influence each other's cellular states through an intercellular communication network (ICN). In cancers, intercellular communications underlie immune evasion mechanisms of individual tumors. We developed an individualized causal analysis framework for discovering tumor specific ICNs. Using head and neck squamous cell carcinoma (HNSCC) tumors as a testbed, we first mined single-cell RNA-sequencing data to discover gene expression modules (GEMs) that reflect the states of transcriptomic processes within tumor and stromal single cells. By deconvoluting bulk transcriptomes of HNSCC tumors profiled by The Cancer Genome Atlas (TCGA), we estimated the activation states of these transcriptomic processes in individual tumors. Finally, we applied individualized causal network learning to discover an ICN within each tumor. Our results show that cellular states of cells in TMEs are coordinated through ICNs that enable multi-way communications among epithelial, fibroblast, endothelial, and immune cells. Further analyses of individual ICNs revealed structural patterns that were shared across subsets of tumors, leading to the discovery of 4 different subtypes of networks that underlie disparate TMEs of HNSCC. Patients with distinct TMEs exhibited significantly different clinical outcomes. Our results show that the capability of estimating individual ICNs reveals heterogeneity of ICNs and sheds light on the importance of intercellular communication in impacting disease development and progression.

SELF-REPORTED DENTAL CONDITIONS AND MEMORY DECLINE: THE MEDIATION ROLE OF STROKE

Abstract The relationship between oral health and cognitive function has been studied extensively; however, little research has examined the underlying pathways. Using a cohort of 6,403 adults aged 51+ from the 2006-2010 English Longitudinal Study of Ageing, we analyzed the effect of changes in self-rated dental conditions on memory function (ranges 0-20) and the mediation effect of stroke using a causal mediation analysis framework. Controlling for socio-demographics, lifestyle, and health status, we found that compared with participants whose dental conditions remained the same, those who reported deterioration of dental conditions in 2006-2010 (N=1,755) experienced a steeper decline in memory function, with an average total effect of -0.22 (95% CI=-0.36, -0.07). The stroke incident had an average mediation effect of -0.007 (95% CI=-0.014, -0.001), representing 3% of the average total effect. The findings accentuate the importance of dental care access for older adults with exacerbated oral health problems.

A systematic nonstationary causality analysis framework for root cause diagnosis of faults in manufacturing processes

Due to production planning adjustments and equipment faults, most variables in the manufacturing processes exhibit nonstationary characteristics. The fault may be covered by the nonstationary trends of variables, making it difficult to identify fault propagation paths and locate root causes by means of traditional fault diagnosis methods. Thus, a systematic causality analysis framework is designed for root cause diagnosis of faults, which fully considers the nonstationarity of time series after faults and the propagation characteristics of faults in manufacturing processes. To be specific, the Kwiatkowski–Phillips–Schmidt–Shin and augmented Dickey–Fuller tests are firstly combined for assessing whether the industrial time series after faults are stationary or not. Then, for the nonstationary variables, the Gonzalo–Granger decomposition is used for extracting stationary factors. Furthermore, the stationary variables and stationary factors are merged into a new data set, an attention based gated recurrent unit is proposed for nonlinear and dynamic causality analysis aiming at root cause diagnosis of faults. Finally, a typical manufacturing process, the hot rolling process is used for verification, and the simulation and comparison results show the practicability and feasibility of the proposed scheme.