Causal Graph Model Research Articles

Efficacy of intelligent diagnosis with a dynamic uncertain causality graph model for rare disorders of sex development.

Disorders of sex development (DSD) are a group of rare complex clinical syndromes with multiple etiologies. Distinguishing the various causes of DSD is quite difficult in clinical practice, even for senior general physicians because of the similar and atypical clinical manifestations of these conditions. In addition, DSD are difficult to diagnose because most primary doctors receive insufficient training for DSD. Delayed diagnoses and misdiagnoses are common for patients with DSD and lead to poor treatment and prognoses. On the basis of the principles and algorithms of dynamic uncertain causality graph (DUCG), a diagnosis model for DSD was jointly constructed by experts on DSD and engineers of artificial intelligence. "Chaining" inference algorithm and weighted logic operation mechanism were applied to guarantee the accuracy and efficiency of diagnostic reasoning under incomplete situations and uncertain information. Verification was performed using 153 selected clinical cases involving nine common DSD-related diseases and three causes other than DSD as the differential diagnosis. The model had an accuracy of 94.1%, which was significantly higher than that of interns and third-year residents. In conclusion, the DUCG model has broad application prospects as a computer-aided diagnostic tool for DSD-related diseases.

A Process Model of Causal Reasoning.

How do we make causal judgments? Many studies have demonstrated that people are capable causal reasoners, achieving success on tasks from reasoning to categorization to interventions. However, less is known about the mental processes used to achieve such sophisticated judgments. We propose a new process model-the mutation sampler-that models causal judgments as based on a sample of possible states of the causal system generated using the Metropolis-Hastings sampling algorithm. Across a diverse array of tasks and conditions encompassing over 1,700 participants, we found that our model provided a consistently closer fit to participant judgments than standard causal graphical models. In particular, we found that the biases introduced by mutation sampling accounted for people's consistent, predictable errors that the normative model by definition could not. Moreover, using a novel experimental methodology, we found that those biases appeared in the samples that participants explicitly judged to be representative of a causal system. We conclude by advocating sampling methods as plausible process-level accounts of the computations specified by the causal graphical model framework and highlight opportunities for future research to identify not just what reasoners compute when drawing causal inferences, but also how they compute it.

Simulating Gene Silencing through Intervention Analysis

SummaryWe propose a novel method for simulating the effects of gene silencing. Our approach combines relevant subject matter information provided by biological pathways with gene expression levels measured in regular conditions to predict the behaviour of the system after one of the genes has been silenced. We achieve this by modelling gene silencing as an external intervention in a causal graphical model. To account for the uncertainty that is associated with the structure learning of the graphical model, we adopt a bootstrap approach. We illustrate our proposal on a Drosophila melanogaster gene silencing experiment.

Dynamic uncertain causality graph based on Intuitionistic fuzzy sets and its application to root cause analysis

Dynamic uncertain causality graph (DUCG), which is based on probability theory, is used for uncertain knowledge representation and reasoning. However, the traditional DUCG has difficulty expressing the causality of the events with crisp numbers. Therefore, an intuitionistic fuzzy set based dynamic uncertain causality graph (IFDUCG) model is proposed in this paper. The model focuses on describing the uncertain event in the form of intuitionistic fuzzy sets, which can handle with the problem of describing vagueness and uncertainty of an event in the traditional model. Then the technique for order preference by similarity to an ideal solution (TOPSIS) method is combined with IFDUCG for knowledge representation and reasoning so as to integrate more abundant experienced knowledge into the model to make the model more reliable. Then some examples are used to validate the proposed method. The experimental results prove that the proposed method is effective and flexible in dealing with the difficulty of the fuzzy event of knowledge representation and reasoning. Furthermore, we make a practical application to root cause analysis of aluminum electrolysis and the results show that the proposed method is available for workers to make decisions.

Mass Evidence Accumulation and Traveler Risk Scoring Engine in e-Border Infrastructure

This paper is concerned with mass evidence accumulation and risk assessment in a particular component of transportation systems and e-borders. We outline the challenges faced by contemporary border control technology and conduct a series of demonstrative experiments that cover critical scenarios, tasks, and states of both evidence accumulation and the traveler risk scoring engine. Using technology gap navigator methodology, this paper suggests an approach to traveler risk estimation based on a unified inference platform, such as a causal graphical model with various incorporated metrics of uncertainty.

Beyond Markov: Accounting for independence violations in causal reasoning

Although many theories of causal cognition are based on causal graphical models, a key property of such models—the independence relations stipulated by the Markov condition—is routinely violated by human reasoners. This article presents three new accounts of those independence violations, accounts that share the assumption that people’s understanding of the correlational structure of data generated from a causal graph differs from that stipulated by causal graphical model framework. To distinguish these models, experiments assessed how people reason with causal graphs that are larger than those tested in previous studies. A traditional common cause network (Y1←X→Y2) was extended so that the effects themselves had effects (Z1←Y1←X→Y2→Z2). A traditional common effect network (Y1→X←Y2) was extended so that the causes themselves had causes (Z1→Y1→X←Y2←Z2). Subjects’ inferences were most consistent with the beta-Q model in which consistent states of the world—those in which variables are either mostly all present or mostly all absent—are viewed as more probable than stipulated by the causal graphical model framework. Substantial variability in subjects’ inferences was also observed, with the result that substantial minorities of subjects were best fit by one of the other models (the dual prototype or a leaky gate models). The discrepancy between normative and human causal cognition stipulated by these models is foundational in the sense that they locate the error not in people’s causal reasoning but rather in their causal representations. As a result, they are applicable to any cognitive theory grounded in causal graphical models, including theories of analogy, learning, explanation, categorization, decision-making, and counterfactual reasoning. Preliminary evidence that independence violations indeed generalize to other judgment types is presented.

Intelligent diagnosis of jaundice with dynamic uncertain causality graph model.

Jaundice is a common and complex clinical symptom potentially occurring in hepatology, general surgery, pediatrics, infectious diseases, gynecology, and obstetrics, and it is fairly difficult to distinguish the cause of jaundice in clinical practice, especially for general practitioners in less developed regions. With collaboration between physicians and artificial intelligence engineers, a comprehensive knowledge base relevant to jaundice was created based on demographic information, symptoms, physical signs, laboratory tests, imaging diagnosis, medical histories, and risk factors. Then a diagnostic modeling and reasoning system using the dynamic uncertain causality graph was proposed. A modularized modeling scheme was presented to reduce the complexity of model construction, providing multiple perspectives and arbitrary granularity for disease causality representations. A "chaining" inference algorithm and weighted logic operation mechanism were employed to guarantee the exactness and efficiency of diagnostic reasoning under situations of incomplete and uncertain information. Moreover, the causal interactions among diseases and symptoms intuitively demonstrated the reasoning process in a graphical manner. Verification was performed using 203 randomly pooled clinical cases, and the accuracy was 99.01% and 84.73%, respectively, with or without laboratory tests in the model. The solutions were more explicable and convincing than common methods such as Bayesian Networks, further increasing the objectivity of clinical decision-making. The promising results indicated that our model could be potentially used in intelligent diagnosis and help decrease public health expenditure.

On the Limits of Causal Modeling: Spatially-Structurally Complex Biological Phenomena

This article examines the adequacy of causal graph theory as a tool for modeling biological phenomena. I argue that the causal graph approach reaches its limits when it comes to modeling biological phenomena that involve complex spatial and chemical-structural relations. Using a case study from molecular biology, I show why causal graph models fail to adequately represent and explain biological phenomena of this kind. The inadequacy of these models is due to their failure to include relevant spatial-structural information in a way that does not render the models nonexplanatory, unmanageable, or inconsistent with basic assumptions of causal graph theory.

Rethinking the Meaning of Concentration-Response Functions and the Estimated Burden of Adverse Health Effects Attributed to Exposure Concentrations.

Four articles by Anenberg etal., Fann etal., Shin etal., and Smith contribute valuable perspectives and syntheses to a large and growing literature that estimates the burden of mortality risks attributed to fine particulate matter (PM2.5) based on estimated epidemiological associations, summarized as concentration-response (C-R) relations. This comment questions the use of C-R relations to predict or estimate how changing exposure concentrations would change responses in a population. C-R associations typically reflect modeling choices, and equally good choices can commonly lead to conflicting conclusions about the signs, significance, and magnitudes of C-R relations and regression coefficients. This indicates that C-R relations do not necessarily reflect underlying stable causal laws useful for making risk predictions, but only choices about how to describe past data, with no uniquely correct choice being determined by the data. Similarly, currently available C-R data typically do not suffice to make valid predictions about how future changes in concentrations will affect responses. These difficulties can be substantially overcome by model ensemble and causal graph modeling and time series methods, but these require different data and knowledge-for example, knowledge of how multiple variables depend on each other, rather than only of how one dependent variable is associated with multiple explanatory variables-than that captured by traditional C-R models or expressible by any single C-R coefficient or curve.

Models of causal inference: Imperfect but applicable is better than perfect but inapplicable

We assess a recent paper by Durand and Vaara (2009) that advances causal graph modeling as a tool for inferring causes in strategy research. We focus on the Markov condition, a key assumption on which causal graph modeling is based, and show why this condition is invariably violated in strategic management in general and the resource‐based view of the firm in particular. We then introduce vector space modeling as a quantitative alternative to causal graph modeling, and consider how improved methods of causal inference might enhance our ability to test some of the central propositions of the resource‐based view. Copyright © 2013 John Wiley & Sons, Ltd.

Identification of Causal Effect with the Non-Compliance and Its <i>EM</i> Algorithm

Many practical studies in biology, medicine, behavior science and the social sciences seek to establish causal relationship between treatments and outcomes, rather than mere associations. In this paper, we use a graphical model to describe a causal graphical model and study its identification. For an unidentifiable model, we introduce covariates which are always observed into the model so that it becomes identifiable. We then give an identifiable condition of the causal graphical model and prove it mathematically. Finally, we give the <img src=image/abs1.png></img>algorithm for the identifiable average causal effect of outcomes to the accepted treatment and give an example to illustrate this method and algorithm.

Improving Causal Inferences in Risk Analysis

Recent headlines and scientific articles projecting significant human health benefits from changes in exposures too often depend on unvalidated subjective expert judgments and modeling assumptions, especially about the causal interpretation of statistical associations. Some of these assessments are demonstrably biased toward false positives and inflated effects estimates. More objective, data-driven methods of causal analysis are available to risk analysts. These can help to reduce bias and increase the credibility and realism of health effects risk assessments and causal claims. For example, quasi-experimental designs and analysis allow alternative (noncausal) explanations for associations to be tested, and refuted if appropriate. Panel data studies examine empirical relations between changes in hypothesized causes and effects. Intervention and change-point analyses identify effects (e.g., significant changes in health effects time series) and estimate their sizes. Granger causality tests, conditional independence tests, and counterfactual causality models test whether a hypothesized cause helps to predict its presumed effects, and quantify exposure-specific contributions to response rates in differently exposed groups, even in the presence of confounders. Causal graph models let causal mechanistic hypotheses be tested and refined using biomarker data. These methods can potentially revolutionize the study of exposure-induced health effects, helping to overcome pervasive false-positive biases and move the health risk assessment scientific community toward more accurate assessments of the impacts of exposures and interventions on public health.

Bond Graph Modeling of a Two-Stage Pressure Relief Valve

This study examined the use of bond graphs for the modeling and simulation of a fluid power system component. A new method is presented for creating the bond graph model, based upon a previously developed mathematical model. A nonlinear dynamic bond graph model for a two-stage pressure relief valve has been developed in this paper. Bond graph submodels were constructed considering each element of the studied valve assembly. The overall bond graph model of the valve was developed by combining these submodels using junction structures. Causality was then assigned in order to obtain a computational model, which could be simulated. The simulation results of the causal bond graph model were compared with those of a mathematical model, which had been also developed in this paper based on the same assumptions. The results were found to correlate very well both in the shape of the curves, magnitude, and response times. The causal bond graph model was verified experimentally in the dynamic mode of operation. As a result of comparison, bond graphs can quickly and accurately model the dynamics in a fluid power control system component. During the simulation study, it was found that nonlinearity occur due to three factors: changes in pressure, which cause nonlinear velocity changes of the flow rate; changes in the throttling area of the valve restriction, which usually changes nonlinearly; and changes in the discharge coefficient of the throttling area of the valve restriction, which does not remain constant.

Leakage in data mining

Deemed “one of the top ten data mining mistakes”, leakage is the introduction of information about the data mining target that should not be legitimately available to mine from. In addition to our own industry experience with real-life projects, controversies around several major public data mining competitions held recently such as the INFORMS 2010 Data Mining Challenge and the IJCNN 2011 Social Network Challenge are evidence that this issue is as relevant today as it has ever been. While acknowledging the importance and prevalence of leakage in both synthetic competitions and real-life data mining projects, existing literature has largely left this idea unexplored. What little has been said turns out not to be broad enough to cover more complex cases of leakage, such as those where the classical independently and identically distributed (i.i.d.) assumption is violated, that have been recently documented. In our new approach, these cases and others are explained by explicitly defining modeling goals and analyzing the broader framework of the data mining problem. The resulting definition enables us to derive general methodology for dealing with the issue. We show that it is possible to avoid leakage with a simple specific approach to data management followed by what we call a learn-predict separation, and present several ways of detecting leakage when the modeler has no control over how the data have been collected. We also offer an alternative point of view on leakage that is based on causal graph modeling concepts.

A Decomposition Method for Nonlinear Parameter Estimation in TRANSCEND

Fault isolation and identification are necessary components for system reconfiguration and fault adaptive control in complex systems. However, accurate and timely on-line fault identification in nonlinear systems can be difficult and computationally expensive. In this paper, we improve the quantitative fault identification scheme in the TRANSCEND diagnosis approach. First, we propose to use possible conflicts (PCs) to find the set of minimally redundant subsystems that can be used for parameter estimation. Second, we introduce new algorithms for computing PCs from the temporal causal graph model used in TRANSCEND. Third, we use the minimal estimators to decompose the system model into smaller, independent subsystems for the parameter estimation task. We demonstrate the feasibility of this method by running experiments on a simulated model of the reverse osmosis subsystem of the advanced water recovery system developed at the NASA Johnson Space Center. Our results show a considerable reduction in parameter estimation time without loss of accuracy and robustness in the estimation.

Discussion around the Scattering Matrix Realization of a Microwave Filter using the Bond Graph Approach and Scattering Formalism

Problem statement: Further to research works made previously and which use collectively the scattering formalism and bond graph technique for the modeling of a physical systems often working in high frequencies, we propose, in this article, a comparative study (discussion) for the scattering matrix realization of a high-frequency physical system. Approach: This discussion is based, on the one hand, on a non-causal (acausal) bond graph model which represents the starting model for the determination of the scattering parameters. On the other hand, we shall use a causal bond graph model richer in information and to which we shall apply what we called in former articles: the analytical procedure of the scattering parameters exploitation with the aim of showing the importance of the causality notion in the physical systems study by the bond graph approach, as well as the importance of the ways and causal loops notion. Results: We will, initially, apply this discussion, to an elementary transmission line; in the second place, the application is carried out on the equivalent circuit of a band pass filter based on localized elements often used like microwave filters in high frequencies studies. Conclusion: We will finish this discussion by realizing the scattering bond graph model of a quadruple by pointing out the procedure used for the construction of this new type of bond graph model.

Assessing statistical significance in causal graphs

BackgroundCausal graphs are an increasingly popular tool for the analysis of biological datasets. In particular, signed causal graphs--directed graphs whose edges additionally have a sign denoting upregulation or downregulation--can be used to model regulatory networks within a cell. Such models allow prediction of downstream effects of regulation of biological entities; conversely, they also enable inference of causative agents behind observed expression changes. However, due to their complex nature, signed causal graph models present special challenges with respect to assessing statistical significance. In this paper we frame and solve two fundamental computational problems that arise in practice when computing appropriate null distributions for hypothesis testing.ResultsFirst, we show how to compute a p-value for agreement between observed and model-predicted classifications of gene transcripts as upregulated, downregulated, or neither. Specifically, how likely are the classifications to agree to the same extent under the null distribution of the observed classification being randomized? This problem, which we call "Ternary Dot Product Distribution" owing to its mathematical form, can be viewed as a generalization of Fisher's exact test to ternary variables. We present two computationally efficient algorithms for computing the Ternary Dot Product Distribution and investigate its combinatorial structure analytically and numerically to establish computational complexity bounds.Second, we develop an algorithm for efficiently performing random sampling of causal graphs. This enables p-value computation under a different, equally important null distribution obtained by randomizing the graph topology but keeping fixed its basic structure: connectedness and the positive and negative in- and out-degrees of each vertex. We provide an algorithm for sampling a graph from this distribution uniformly at random. We also highlight theoretical challenges unique to signed causal graphs; previous work on graph randomization has studied undirected graphs and directed but unsigned graphs.ConclusionWe present algorithmic solutions to two statistical significance questions necessary to apply the causal graph methodology, a powerful tool for biological network analysis. The algorithms we present are both fast and provably correct. Our work may be of independent interest in non-biological contexts as well, as it generalizes mathematical results that have been studied extensively in other fields.

New extraction method of the scattering parameters of a physical system starting from its causal bond graph model: Application to a microwave filter

The general idea consists of using jointly the bond graph technique and the scattering formalism. For that, we propose to extract the scattering parameters of a microwave filter with localized or distributed elements from its causal bond graph model while starting with the determination of the integro-differentials operators which is based, in their determination, on the causal ways and causal loops present in the associated bond graph model, and which gives rise to the wave matrix which gathers the incident and reflected waves propagation of the studied filter. The scattering parameters, founded from the wave matrix, were checked by comparison of the simulation results. Thereafter, we used a procedure to model these scattering parameters on a particular type of bond graph model often named Scattering Bond Graph Model. Key words: Waves propagation, bond graph modelling, high frequency domain, scattering parameters, integro-differentials operators, microwave filter, modelling and simulation.

How to Find Wave-Scattering Parameters from the Causal Bond Graph Model of a High Frequency Filter

Problem statement: The aim of this study is to use the bond graph technique and the wave-scattering formalism, jointly, to determine the scattering matrix of a high frequency filter with cut-off frequency 10 GHz. Approach: The first step consist to model the high frequency filter by bond graph approach, afterward, we decomposed the obtained causal bond graph model under an elementary structures of a cascaded sub-model, where, each one containing only 0-junction (parallel admittance) and 1-junction (series impedance). The causal ways and algebraic loops present in each causal bond graph sub-model, allows us to derive the elementary wave matrix W(i) of each sub-model. Results: The total or global wave matrix W(T) of the high frequency filter is given by the product of all elementary wave matrixes W(i). Conclusion: The scattering matrix S of the studied filter can be inferred by use of the founded wave matrix W(T) which relates the incident and reflected waves at one port directly to the incident and reflected waves at the adjacent port and the relations linking the S coefficients to the W coefficients. Finally, the scattering parameters, founded from the wave matrix, will be checked by comparison of the simulation results.

Modeling Method of a Low-Pass Filter Based on Microstrip T-Lines with Cut-Off Frequency 10 GHz by the Extraction of its Wave-Scattering Parameters from its Causal Bond Graph Model

Problem statement: This study presented a jointly application of bond graph technique and wave-scattering formalism for a new realization called scattering bond graph model which has the main advantage to show up explicitly the different wave propagation. Approach: For that, we proposed to find the scattering matrix from the causal bond graph model of a low-pass filter based on Microstrip lines and with cut-off frequency 10 GHz, while starting with determination of the integro-differentials operators which is based, in their determination, on the causal ways and causal algebraic loops present in the associated bond graph model and which gives rise to the wave matrix which gathers the incident and reflected waves propagation of the studied filter. Results: The scattering parameters, founded from the wave matrix, will be checked by comparison of the simulation results. Conclusion: Thereafter, we use a procedure to model directly this scattering matrix under a special bond graph model form often called “Scattering Bond Graph Model”.