Causal Considerations Research Articles

From Generative AI to Objective-Driven Systems: A Paradigm Shift in Artificial Intelligence

Marwan Omar 1Illinois Institute of Technology Email: momar3@iit.edu Abstract The rapid advancement of Artificial Intelligence (AI), particularly in the domain of generative models, has led to impressive achievements in content creation and natural language processing. However, these models are inherently limited by their reliance on pattern recognition and lack of true understanding. In contrast, Objective-Driven AI offers a promising alternative by focusing on goal-oriented behavior, causal reasoning, and the development of world models. This paper explores the limitations of generative AI, highlighting its inability to grasp context, causality, and ethical considerations. It then presents the concept of Objective-Driven AI, emphasizing its potential to operate effectively in complex, real-world environments where understanding and reasoning are critical. The paper concludes with a discussion of future research directions, including advanced world modeling techniques, ethical AI, and robustness against adversarial attacks, which are essential for the further development of Objective-Driven AI systems. Keywords Objective-Driven AI, Generative AI, Causal Reasoning, World Modeling, Ethical AI, Artificial Intelligence, Adversarial Attacks, Machine Learning, Autonomous Systems

Construction of an adverse outcome pathway for the cardiac toxicity of bisphenol a by using bioinformatics analysis

Bisphenol A (BPA), a common endocrine disruptor, has shown cardiovascular toxicity in several epidemiological studies, as well as in vivo and in vitro experimental studies. However, the related adverse outcome pathway (AOP) of BPA toxicity remains unraveled. This study aimed to develop an AOP for the cardiac toxicity of BPA through bioinformatics analysis. The interactions among BPA, genes, phenotypes, and cardiac toxicity were retrieved from several databases, including the Comparative Toxicogenomics Database, Computational Toxicology, DisGeNet, and MalaCards. The target genes and part of target phenotypes were obtained by Venn analysis and literature screening. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis were performed for target genes by using the DAVID online analysis tool to obtain other target phenotypes. AOP hypotheses from BPA exposure to heart disease were established and evaluated comprehensively by a quantitative weight of evidence (QWOE) method. The target genes included ESR2, MAPK1, TGFB1, and ESR1, and the target phenotypes included heart contraction, cardiac muscle contraction, cellular Ca2+ homeostasis, cellular metabolic process, heart development, etc. Overall, the AOP of BPA cardiac toxicity was deduced to be as follows. Initially, BPA bound with ERα/β and then activated the MAPK, AKT, and IL-17 signaling pathways, leading to Ca2+ homeostasis disorder and increased inflammatory response. Subsequently, cardiac function was impaired, causing coronary heart disease, arrhythmia, cardiac dysplasia, and other heart diseases. According to the Bradford–Hill causal considerations, the score of AOP by QWOE was 69, demonstrating a moderate confidence and providing clues on cardiotoxicity-assessment procedure and further studies on BPA.

Scenario Identification and Classification to Support the Assessment of Advanced Driver Assistance Systems

In recent years, driver assistance systems in cars, buses, and trucks have become more common and powerful. In particular, the introduction of AI methods to sensors, signal fusion, and traffic recognition allows us to step forward from actual level-2 assistance to level-3 Advanced Driver Assistance Systems (ADAS), where driving becomes autonomous and responsibility shifts from the driver to the automobile manufacturers. This, however, requires a high-precision risk assessment of failure, which can only be achieved by extensive data acquisition and statistical analysis of real traffic scenarios (which is impossible to perform by humans). Therefore, critical driving situations have to be identified and classified automatically. This paper develops and compares two different strategies—a traditional rule-based approach derived from deterministic causal considerations, and an AI-based approach trained with idealized cut-in, cut-out, and cut-through maneuvers. Application to a 10-h measurement sequence on a German highway demonstrates that the latter has the higher performance, whereas the former misses some of the safety-relevant events to be identified.

Temporal-spatial-fusion-based risk assessment on the adjacent building during deep excavation

Foundation pit excavation will inevitably cause uneven ground settlement to pose potential risks to adjacent structures and infrastructures. To better perceive the risk status of adjacent buildings using multi-source information, a temporal-spatial-fusion-based risk assessment (TSFRA) model under the consideration of uncertainty and causality is developed by integrating FAHP (Fuzzy Analytic Hierarchy Process), DGDT (DAG-GNN, DEMATEL method, and topological analysis), and CM (cloud model). More specifically, FAHP handles temporal weights of excavation conditions based on expert knowledge. DGDT determines spatial weights for monitoring points, with DAG-GNN constructing causal graphs in a data-driven manner, DEMATEL handling global interactions, and topological analysis calculating node importance. CM can finally fuse the temporal (excavation conditions) and spatial (monitoring points) weights with settlement monitoring data, resulting in qualitative assessments of the overall risk status of adjacent buildings in uncertain environments. The proposed TSFRA is verified in the case of a Shanghai metro station construction project. Results indicate that the perceived risk of the project is at a relatively low level, consistent with the actual condition. High-risk excavation conditions and locations can be easily identified. There is a trend toward higher risk during the excavation of soft soil layers, and thus some risk control measures can be formulated to avoid potential risk events. In short, the weight determination and fusion method in TSFRA contribute to handling uncertainties in expert judgments and causalities in collected data, which can practically provide more reliable decision-making in excavation-induced risk assessment and control for ensuring the safety of adjacent buildings.

Exploring the effects of family and life events on genetic and environmental architecture of schizotypal and hypomanic dimensions: Insights from a twin study

BackgroundStrategies of prevention for psychiatric disorders need a deep understanding of the aetiological factors involved in the psychopathological processes. Our twin study aims at disentangling the contributions of genes and environment to schizotypal and hypomanic dimensions, considering the role of stressful life events (LEs) and the quality of family relationships. MethodsThe Magical Ideation Scale (MIS) and Perceptual Aberration Scale (PAS) were used to assess positive schizotypy, while Hypomanic Personality Scale (HPS) and its sub-scales were used to investigate proneness to affective disorders. 268 twins (54.5 % female; aged 18.0 ± 6.68) were included. Participants filled out a questionnaire on LEs and their parents provided an evaluation of intra-family relationship (Relationship Quality Index, RQI). Classic univariate twin models for quantitative traits were fitted for scales, and the effects of covariates (LEs and RQI) were assessed. ResultsFor MIS, HPS and its sub-scales, significant common and unique environmental effects were detected, with genetic factors affecting only HPS Social Vitality sub-scale. Unique environment was the only source of variance of PAS score. The number of recent LEs influenced MIS and PAS models, while RQI score affected MIS model. LimitationsThe main limitation of the study is the small sample size, which reduces statistical power and may potentially lead to an underestimation of heritability. Additionally, the cross-sectional design limits the possibility to draw causal considerations. ConclusionsFindings provide preliminary evidence for a significant environmental role in modulating states of vulnerability. Moreover, the expression of positive schizotypy resulted influenced by recent stressors and intra-family relationships.

Blameworthiness and Causal Outcomes

AbstractIt is widely held that whether a person is morally responsible for an outcome partly depends on whether certain causal relations obtain between that person and the outcome. This paper argues that, regardless of whether the preceding claim about moral responsibility is true, moral blameworthiness is independent of such causal considerations. This conclusion is motivated by considering cases from Carolina Sartorio and Sara Bernstein. The causal structures of these cases are complex. Sartorio and Bernstein believe that reaching conclusions about moral responsibility and blameworthiness requires wading through this complexity. But it is argued here that getting clear on these causal structures does not help us assess blameworthiness (regardless of the relevance of such considerations for drawing conclusions about moral responsibility). Moral blameworthiness simply does not turn on the considerations relevant to figuring out whether an agent causally contributed to an outcome.

Estimating the Impact of Rurality in Disparities in Cancer Mortality.

Estimation of the independent effect of rurality on cancer mortality requires causal inference methodology and consideration of area-level socioeconomic status and rural designations. Using SEER data, we identified key incident cancers diagnosed between 2000 and 2016 at age ≥20 years (N = 3,788,273), examining a 20% random sample (n = 757,655). Standardized competing risk and survival models estimated the association between rural residence, defined by Rural-Urban Continuum Codes, and cancer-specific and all-cause mortality, controlling for age at cancer diagnosis, sex, race/ethnicity, year of diagnosis, and Area Deprivation Index (ADI). We estimated the attributable fraction (AF) of rurality and high ADI (ADI > median) to the probability of mortality. Finally, we examined county measurement issues contributing to mortality rates discordant from hypothesized rates. The 5-year standardized failure probability for cancer mortality for rural patients was 33.9% versus 31.56% for urban. The AF for rural residence was 1.04% at year 1 (0.89% by year 5), the highest among local stage disease (Y1 2.1% to Y5 1.9%). The AF for high ADI was 3.33% in Y1 (2.87% in Y5), while the joint effect of rural residence and high ADI was 4.28% in Y1 (3.71% in Y5). Twenty-two percent of urban counties and 30% of rural were discordant. Among discordant urban counties, 30% were only considered urban because of adjacency to metro area. High ADI was associated with urban discordance and low ADI with rural discordance. Rural residence independently contributes to cancer mortality. The rural impact is the greatest among those with localized disease and in high deprivation areas. Rural-urban county designations may mask high-need urban counties, limiting eligibility to state and federal resources dedicated to rural areas.

Exploring integral bounds for sound absorption: A comprehensive study

Sound absorption across a wide range of frequencies is a focus in contemporary acoustics. Recently, integral bounds of absorption or reflection coefficients were introduced as a guide of design optimization following the footsteps of electromagnetics, where integral relations were derived based on system causality considerations. This talk examines the relation between the integral bound and the effects of various physical boundary conditions and the bulk absorber material, with the hope to raise the bound. Effects of various approximations made during mathematical derivation are also examined by comparison with specific numerical examples, and the physics of the bound is thoroughly discussed. The findings are expected to have significant implications for the development of effective noise reduction strategies and the advancement of smart acoustic design.

Direct Determination of Photonic Stopband Topological Character: A Framework Based on Dispersion Measurements

Ascertainment of photonic stopband absolute topological character requires information regarding the Bloch eigenfunction spatial distribution. Consequently, the experimental investigations predominantly restrict themselves to the bulk‐boundary correspondence principle and the ensuing emergence of topological surface state. Although capable of establishing the equivalence/inequivalence of bandgaps, the determination of their absolute topological identity remains out of its purview. The alternate method of reflection phase‐based identification also provides only contentious improvements owing to the measurement complexities pertaining to the interferometric setups. To circumvent these limitations, the Kramers–Kronig amplitude‐phase causality considerations are resorted to and an experimentally conducive method is proposed for bandgap topological character determination directly from the parametric reflectance measurements. Particularly, it is demonstrated that in case of 1D photonic crystals, polarization‐resolved dispersion measurements suffice in qualitatively determining bandgaps’ absolute topological identities. By invoking the translational invariance of the investigated samples, a parameter “differential effective mass” is also defined, that encapsulates bandgaps’ topological identities and engenders an experimentally discernible bandgap classifier.

Nonlinear causal network learning via Granger causality based on extreme support vector regression.

For complex networked systems, based on the consideration of nonlinearity and causality, a novel general method of nonlinear causal network learning, termed extreme support vector regression Granger causality (ESVRGC), is proposed. The nonuniform time-delayed influence of the driving nodes on the target node is particularly considered. Then, the restricted model and the unrestricted model of Granger causality are, respectively, formulated based on extreme support vector regression, which uses the selected time-delayed components of system variables as the inputs of kernel functions. The nonlinear conditional Granger causality index is finally calculated to confirm the strength of a causal interaction. Generally, based on the simulation of a nonlinear vector autoregressive model and nonlinear discrete time-delayed dynamic systems, ESVRGC demonstrates better performance than other popular methods. Also, the validity and robustness of ESVRGC are also verified by the different cases of network types, sample sizes, noise intensities, and coupling strengths. Finally, the superiority of ESVRGC is successful verified by the experimental study on real benchmark datasets.

Integrating Hill’s classical considerations with modern causal inference methods in observational studies: a ‘How-Questions’ framework

Abstract Context Modern causal inference methods – although core to epidemiological reasoning – may be difficult to master and less intuitive than Hill’s classical considerations. We developed a ‘How-Questions’ (HQ) framework to integrate Hill's classical considerations with modern causal inference methods in observational studies. Methods First, we extracted the main causal considerations from contemporary philosophy of science: characteristics of empirical associations, universality, depth, and degree of corroboration of a theory. From these, we developed a HQ framework based on six domains formulated as questions: (1) how valid?, (2) how time-ordered?, (3) how big?, (4) how shaped?, (5) how replicable?, and (6) how explainable? Then, we qualitatively checked whether Hill's classical considerations and key selected modern causal inference methods were compatible with the HQ framework. Lastly, as a proof-of-concept, we applied the HQ framework to two observational studies of current topics in epidemiology. Findings Both Hill’s considerations and key selected modern causal inference methods were compatible with the six domains of the HQ framework. (1) The how-valid domain is addressed by considering the same internal validity issues in Hill’s and modern methods, namely confounding, selection and measurement biases; modern methods use more formalized techniques, including quantitative bias analyses/sensitivity analyses (QBA/SA). (2) The how-time-ordered domain is addressed by considering reverse causation in Hill’s; modern methods may use G methods within the context of longitudinal data analyses and time-varying exposures. (3) The how-big domain is addressed by strength of association in Hill’s; modern methods first consider estimands and may use QBA/SA to assess robustness of effect estimates. (4) The how-shaped domain is represented by biological gradient in Hill’s; modern methods may use generalized propensity scores to estimate dose-response functions. (5) The how-replicable domain is addressed in Hill’s by consistency of study findings with existing evidence; modern methods may use triangulation of different study designs and consider generalizability and transportability concepts. (6) The how-explainable domain is addressed by biological plausibility in Hill’s and by mediation/interaction analyses in modern methods. The application of the HQ framework to two observational studies provides a proof-of-concept and suggests its potential usefulness to integrate Hill’s considerations with modern causal inference methods. Perspective We found that the six dimensions of the HQ framework integrated Hill’s classical considerations with modern causal inference methods for observational studies. Apart from its potential pedagogical value, the HQ framework may provide a holistic view for the causal assessment of observational studies in epidemiology.

STHSGCN: Spatial-temporal heterogeneous and synchronous graph convolution network for traffic flow prediction

Nowadays, as a crucial component of intelligent transportation systems, traffic flow prediction has received extensive concern. However, most of the existing studies extracted spatial-temporal features with modules that do not differentiate with time and space, and failed to consider spatial-temporal heterogeneities. Furthermore, although previous works have achieved synchronous modeling of spatial-temporal dependencies, the consideration of temporal causality is still lacking in their graph structures. To address these shortcomings, a spatial-temporal heterogeneous and synchronous graph convolution network (STHSGCN) is proposed for traffic flow prediction. To be specific, separate dilated causal spatial-temporal synchronous graph convolutional networks (DCSTSGCNs) for various node clusters are designed to reflect spatial heterogeneity, different dilated causal spatial-temporal synchronous graph convolutional modules (DCSTSGCMs) for diverse time steps are deployed to take account of temporal heterogeneity. In addition, causal spatial-temporal synchronous graph (CSTSG) is proposed to capture temporal causality in spatial-temporal synchronous learning. We further conducted extensive experiments on four real-world datasets, and the results verified the consistent superiority of our proposed approach compared with various existing baselines.

How the Complexity of Psychological Processes Reframes the Issue of Reproducibility in Psychological Science

In the past decade, various recommendations have been published to enhance the methodological rigor and publication standards in psychological science. However, adhering to these recommendations may have limited impact on the reproducibility of causal effects as long as psychological phenomena continue to be viewed as decomposable into separate and additive statistical structures of causal relationships. In this article, we show that (a) psychological phenomena are patterns emerging from nondecomposable and nonisolable complex processes that obey idiosyncratic nonlinear dynamics, (b) these processual features jeopardize the chances of standard reproducibility of statistical results, and (c) these features call on researchers to reconsider what can and should be reproduced, that is, the psychological processes per se, and the signatures of their complexity and dynamics. Accordingly, we argue for a greater consideration of process causality of psychological phenomena reflected by key properties of complex dynamical systems (CDSs). This implies developing and testing formal models of psychological dynamics, which can be implemented by computer simulation. The scope of the CDS paradigm and its convergences with other paradigms are discussed regarding the reproducibility issue. Ironically, the CDS approach could account for both reproducibility and nonreproducibility of the statistical effects usually sought in mainstream psychological science.

A historical note about the sufficient cause model with special consideration of the work of Max Verworn 1912

The aim of this paper is to present the historical roots of the sufficient component cause model, which is very well known in epidemiology. I have analyzed Max Verworn's writings on the description of the sufficient component cause model. Verworn introduced a precursor of the sufficient component cause model as early as 1912, possibly inspired by Ernst Mach. He argued for the abandonment of the concept of "cause" (singular). Instead, he preferred the term "conditions." Unlike Karl Pearson, Verworn was not opposed to causal considerations. However, according to Verworn, every process or state is determined by numerous conditions and not by a single factor or "cause." Instead of speaking of causalism, Verworn preferred to speak of conditionalism. The earliest description of the concept of the sufficient component cause model known in the epidemiological literature since 1976 dates back at least to 1912.

Causal inference based cuffless blood pressure estimation: A pilot study

Enabled by wearable sensing, e.g., photoplethysmography (PPG) and electrocardiography (ECG), and machine learning techniques, study on cuffless blood pressure (BP) measurement with data-driven methods has become popular in recent years. However, causality has been overlooked in most of current studies. In this study, we aim to examine the feasibility of causal inference for cuffless BP estimation. We first attempt to detect wearable features that are causally related, rather than correlated, to BP changes by identifying causal graphs of interested variables with fast causal inference (FCI) algorithm. With identified causal features, we then employ time-lagged link to integrate the mechanism of causal inference into the BP estimated model. The proposed method was validated on 62 subjects with their continuous ECG, PPG and BP signals being collected. We found new causal features that can better track BP changes than pulse transit time (PTT). Further, the developed causal-based estimation model achieved an estimation error of mean absolute difference (MAD) being 5.10 mmHg and 2.85 mmHg for SBP and DBP, respectively, which outperformed traditional model without consideration of causality. To the best of our knowledge, this work is the first to study the causal inference for cuffless BP estimation, which can shed light on the mechanism, method and application of cuffless BP measurement.

Relating a System's Hamiltonian to Its Entropy Production Using a Complex Time Approach.

We exploit the properties of complex time to obtain an analytical relationship based on considerations of causality between the two Noether-conserved quantities of a system: its Hamiltonian and its entropy production. In natural units, when complexified, the one is simply the Wick-rotated complex conjugate of the other. A Hilbert transform relation is constructed in the formalism of quantitative geometrical thermodynamics, which enables system irreversibility to be handled analytically within a framework that unifies both the microscopic and macroscopic scales, and which also unifies the treatment of both reversibility and irreversibility as complementary parts of a single physical description. In particular, the thermodynamics of two unitary entities are considered: the alpha particle, which is absolutely stable (that is, trivially reversible with zero entropy production), and a black hole whose unconditional irreversibility is characterized by a non-zero entropy production, for which we show an alternate derivation, confirming our previous one. The thermodynamics of a canonical decaying harmonic oscillator are also considered. In this treatment, the complexification of time also enables a meaningful physical interpretation of both "imaginary time" and "imaginary energy".

Giant-Size Main Pulmonary Artery Aneurysm in an Adult Patient with Ebstein Anomaly and Dextrocardia

Main pulmonary artery aneurysms are rare, mostly asymptomatic and discovered accidentally. The main pulmonary artery aneurysms may be idiopathic or secondary to underlying diseases such as pulmonary hypertension, Behcet’s disease, connective tissue disorders, congenital heart disease, vasculitis, syphilis, tuberculosis and endocarditis. There are some indices that dextrocardia is associated with anomalies of the pulmonary arteries and pulmonary valve. A rare occurrence of main pulmonary artery aneurysms results in a lack of recommendations, so the remaining challenges are whether main pulmonary artery aneurysms should be treated, how, and when. The decision on surgical intervention or conservative treatment of the main pulmonary artery aneurysms depends on its size, etiology and accompanying diseases and includes a multidisciplinary heart team. Our case of the main pulmonary artery aneurysm and pulmonary valve abnormality associated with Ebstein anomaly and dextrocardia in a 67-year-old male patient brings causality considerations and treatment options in such a unique condition.

Learning causality with graphs

AbstractRecent years have witnessed a rocketing growth of machine learning methods on graph data, especially those powered by effective neural networks. Despite their success in different real‐world scenarios, the majority of these methods on graphs only focus on predictive or descriptive tasks, but lack consideration of causality. Causal inference can reveal the causality inside data, promote human understanding of the learning process and model prediction, and serve as a significant component of artificial intelligence (AI). An important problem in causal inference is causal effect estimation, which aims to estimate the causal effects of a certain treatment (e.g., prescription of medicine) on an outcome (e.g., cure of disease) at an individual level (e.g., each patient) or a population level (e.g., a group of patients). In this paper, we introduce the background of causal effect estimation from observational data, envision the challenges of causal effect estimation with graphs, and then summarize representative approaches of causal effect estimation with graphs in recent years. Furthermore, we provide some insights for future research directions in related area. Link to video abstract: https://youtu.be/BpDPOOqw‐ns

Socioeconomic inequalities in insulin initiation among individuals with type 2 diabetes – A quasi-experimental nationwide register study

BackgroundInequalities in access to care can translate to or strengthen existing inequalities in health if people of lower socioeconomic positions do not have equal access to care. I study insulin initiation among individuals with type 2 diabetes and examine whether a reform increasing the co-payment of non-insulin antidiabetics in Finland in 2017 had an inequitable effect on the initiation. In the treatment of type 2 diabetes, insulin is recommended only in later stages and remains covered by the National Health Insurance at a rate of 100%. Data and methodsI evaluated the effect of the reform with Cox proportional hazard modelling using nationwide person-level register data from 2011 to 2019. Exploiting a quasi-experimental design rising from the introduction of the reform allows for consideration of causality. ResultsI found that the risk of insulin initiation was lower in the later years of the study period. Additionally, individuals in lower socioeconomic positions had a higher risk of initiation. However, I did not find inequalities in how the reform affected the risk of insulin initiation between income quintiles. ConclusionsCo-payments are unlikely to be the most influential factor behind persisting inequalities in insulin initiation among individuals with type 2 diabetes in Finland. Lower risk in the later years aligns with developing treatment practices of type 2 diabetes.

50th Anniversary of TPRC—Research Conference on Communications, Information and Internet Policy: Looking Ahead to the Next 50 Years

50th Anniversary of TPRC—Research Conference on Communications, Information and Internet Policy: Looking Ahead to the Next 50 Years