Causal inference using regression-based statistical control: Confusion in Econometrics

Regression is a widely used econometric tool in research. In observational studies, based on a number of assumptions, regression-based statistical control methods attempt to analyze the causation between treatment and outcome by adding control variables. However, this approach may not produce reliable estimates of causal effects. In addition to the shortcomings of the method, this lack of confidence is mainly related to ambiguous formulations in econometrics, such as the definition of selection bias, selection of core control variables, and method of testing for robustness. Within the framework of the causal models, we clarify the assumption of causal inference using regression-based statistical controls, as described in econometrics, and discuss how to select core control variables to satisfy this assumption and conduct robustness tests for regression estimates.

Depression recognition using functional connectivity based on dynamic causal model

Dynamic casual modeling of functional magnetic resonance imaging（fMRI） signals is employed to explore critical emotional neurocircuitry under sad stimuli. The intrinsic model of emotional loops is built on the basis of Papez＇s circuit and related prior knowledge, and then three modulatory connection models are established. In these models, stimuli are placed at different points, which represents they affect the neural activities between brain regions, and these activities are modulated in different ways. Then, the optimal model is selected by Bayesian model comparison. From group analysis, patients＇ intrinsic and modulatory connections from the anterior cingulate cortex （ACC） to the right inferior frontal gyrus （rlFG） are significantly higher than those of the control group. Then the functional connection parameters of the model are selected as classifier features. The classification accuracy rate from the support vector machine（SVM） classifier is 80.73%, which, to some extent, validates the effectiveness of the regional connectivity parameters for depression recognition and provides a new approach for the clinical diagnosis of depression.

Causal association rule mining methods based on fuzzy state description

Aiming at the research that using more new knowledge to develope knowledge system with dynamic accordance, and under the background of using Fuzzy language field and Fuzzy language values structure as description framework, the generalized cell Automation that can synthetically process fuzzy indeterminacy and random indeterminacy and generalized inductive logic causal model is brought forward. On this basis, a kind of the new method that can discover causal association rules is provded. According to the causal information of standard sample space and commonly sample space, through constructing its state （abnormality） relation matrix, causal association rules can be gained by using inductive reasoning mechanism. The estimate of this algorithm complexity is given,and its validiw is proved through case.

Observed communication between oncologists and patients:A causal model of communication competence

AIM： To investigate and test a causal model derivedfrom previous meta-analytic data of health provider be-haviors and patient satisfaction.METHODS： A literature search was conducted forrelevant manuscripts that met the following criteria：Reported an analysis of provider-patient interaction inthe context of an oncology interview; the study hadto measure at least two of the variables of interest tothe model （provider activity, provider patient-centeredcommunication, provider facilitative communication,patient activity, patient involvement, and patient satis-faction or reduced anxiety）; and the information had tobe reported in a manner that permitted the calculationof a zero-order correlation between at least two of thevariables under consideration. Data were transformedinto correlation coefficients and compiled to producethe correlation matrix used for data analysis. The test of the causal model is a comparison of the expected correlation matrix generated using an Ordinary Least Squares method of estimation. The expected matrix iscompared to the actual matrix of zero order correlation coeffcients. A model is considered a possible ft if the level of deviation is less than expected due to random sampling error as measured by a chi-square statistic. The signifcance of the path coeffcients was tested us-ing a z test. Lastly, the Sobel test provides a test of the level of mediation provided by a variable and provides an estimate of the level of mediation for each connec-tion. Such a test is warranted in models with multiple paths.RESULTS： A test of the original model indicated a lack of ft with the summary data. The largest discrepancy in the model was between the patient satisfaction and the provider patient-centered utterances. The observed correlation was far larger than expected given a medi-ated relationship. The test of a modifed model was un-dertaken to determine possible ft. The corrected model provides a fit to within tolerance as evaluated by the test statistic, χ2 （8, average n = 342） = 10.22. Each of the path coefficients for the model reveals that each one can be considered signifcant, P 〈 0.05. The Sobel test examining the impact of the mediating variables demonstrated that patient involvement is a signifcantmediator in the model, Sobel statistic = 3.56, P 〈 0.05. Patient active was also demonstrated to be a signifcant mediator in the model, Sobel statistic = 4.21, P 〈 0.05. The statistics indicate that patient behavior mediates the relationship between provider behavior and patient satisfaction with the interaction.CONCLUSION： The results demonstrate empirical support for the importance of patient-centered care and satisfy the need for empirical casual support of provider-patient behaviors on health outcomes.

Research on FDI and China＇s Economic Growth

In this paper, the relationship between FDI and China＇s economic growth is analyzed by Granger causality test and multiple regression model. It is found that relationship is bi-directional causal. It is suggested that the utilization of FDI should be focused on not only the quantity, but also the quality of FDI with its rapid development.

A Modeling and Probabilistic Reasoning Method of Dynamic Uncertain Causality Graph for Industrial Fault Diagnosis

Online automatic fault diagnosis in industrial systems is essential for guaranteeing safe, reliable and efficient operations.However, difficulties associated with computational overload, ubiquitous uncertainties and insufficient fault samples hamper the engineering application of intelligent fault diagnosis technology. Geared towards the settlement of these problems, this paper introduces the method of dynamic uncertain causality graph, which is a new attempt to model complex behaviors of real-world systems under uncertainties. The visual representation to causality pathways and self-relied "chaining" inference mechanisms are analyzed. In particular, some solutions are investigated for the diagnostic reasoning algorithm to aim at reducing its computational complexity and improving the robustness to potential losses and imprecisions in observations. To evaluate the effectiveness and performance of this method, experiments are conducted using both synthetic calculation cases and generator faults of a nuclear power plant. The results manifest the high diagnostic accuracy and efficiency, suggesting its practical significance in large-scale industrial applications.

Hybrid-augmented intelligence： collaboration and cognition

The long-term goal of artificial intelligence （AI） is to make machines learn and think like human beings. Due to the high levels of uncertainty and vulnerability in human life and the open-ended nature of problems that humans are facing, no matter how intelligent machines are, they are unable to completely replace humans. Therefore, it is necessary to introduce human cognitive capabilities or human-like cognitive models into AI systems to develop a new form of AI, that is, hybrid-augmented intelligence. This form of AI or machine intelligence is a feasible and important developing model. Hybrid-augmented intelligence can be divided into two basic models： one is human-in-the-loop augmented intelligence with human-computer collaboration, and the other is cognitive computing based augmented intelligence, in which a cognitive model is embedded in the machine learning system. This survey describes a basic framework for human-computer collaborative hybrid-augmented intelligence, and the basic elements of hybrid-augmented intelligence based on cognitive computing. These elements include intuitive reasoning, causal models, evolution of memory and knowledge, especially the role and basic principles of intuitive reasoning for complex problem solving, and the cognitive learning framework for visual scene understanding based on memory and reasoning. Several typical applications of hybrid-augmented intelligence in related fields are given.

Separate Neural Networks for Gains and Losses in Intertemporal Choice

An important and unresolved question is how human brain regions process information and interact with each other in intertemporal choice related to gains and losses. Using psychophysiological interaction and dynamic causal modeling analyses, we investigated the functional interactions between regions involved in the decision- making process while participants performed temporal discounting tasks in both the gains and losses domains. We found two distinct intrinsic valuation systems underlying temporal discounting in the gains and losses domains： gains were specifically evaluated in the medial regions, including the medial prefrontal and orbitofrontal cortices, and losses were evaluated in the lateral dorsolateral prefrontal cortex. In addition, immediate reward or pun- ishment was found to modulate the functional interactions between the dorsolateral prefrontal cortex and distinct regions in both the gains and losses domains： in the gains domain, the mesolimbic regions; in the losses domain, the medial prefrontal cortex, anterior cingulate cortex, and insula. These findings suggest that intertemporal choice of gains and losses might involve distinct valuation systems, and more importantly, separate neural interactions may implement the intertemporal choices of gains and losses. These findings may provide a new biological perspective for understanding the neural mechanisms underlying intertemporal choice of gains and losses.

Effective connectivity of dorsal and ventral visual pathways in chunk decomposition

Chunk decomposition is defined as a cognitive process which breaks up familiar items into several parts to reorganize them in an alternative approach.The present study investigated the effective connectivity of visual streams in chunk decomposition through dynamic causal modeling(DCM).The results revealed that chunk familiarity and perceptual tightness made a combined contribution to highlight not only the "what" and the "where" streams,but also the effective connectivity from the left inferior temporal gyrus to the left superior parietal lobule.

Abnormal Effective Connectivity of the Anterior Forebrain Regions in Disorders of Consciousness

A number of studies have indicated that disor- ders of consciousness result from multifocal injuries as well as from the impaired functional and anatomical connectivity between various anterior forebrain regions. However, the specific causal mechanism linking these regions remains unclear. In this study, we used spectral dynamic causal modeling to assess how the effective connections （ECs） between various regions differ between individuals. Next, we used connectome-based predictive modeling to evaluate the performance of the ECs in predicting the clinical scores of DOC patients. We found increased ECs from the striatum to the globus pallidus as well as from the globus pallidus to the posterior cingulate cortex, and decreased ECs from the globus pallidus to the thalamus and from the medial prefrontal cortex to the striatum in DOC patients as compared to healthy controls. Prediction of the patients＇ outcome was effective using the negative ECs as features. In summary, the present study highlights a key role of the thalamo-basal ganglia-cortical loop in DOCs and supports the anterior forebrain mesocircuit hypothesis. Furthermore, EC could be potentially used to assess the consciousness level.

Abnormal Effective Connectivity in the Brain is Involved in Auditory Verbal Hallucinations in Schizophrenia

Information flow among auditory and language processing-related regions implicated in the pathophysiology of auditory verbal hallucinations（AVHs） in schizophrenia（SZ） remains unclear. In this study, we used stochastic dynamic causal modeling（s DCM） to quantify connections among the left dorsolateral prefrontal cortex（inner speech monitoring）, auditory cortex（auditory processing）, hippocampus（memory retrieval）, thalamus（information filtering）, and Broca＇s area（language production） in 17 first-episode drug-na？¨ve SZ patients with AVHs, 15 without AVHs, and 19 healthy controls using resting-state functional magnetic resonance imaging.Finally, we performed receiver operating characteristic（ROC） analysis and correlation analysis between image measures and symptoms. s DCM revealed an increasedsensitivity of auditory cortex to its thalamic afferents and a decrease in hippocampal sensitivity to auditory inputs in SZ patients with AVHs. The area under the ROC curve showed the diagnostic value of these two connections to distinguish SZ patients with AVHs from those without AVHs. Furthermore, we found a positive correlation between the strength of the connectivity from Broca＇s area to the auditory cortex and the severity of AVHs. These findings demonstrate, for the first time, augmented AVHspecific excitatory afferents from the thalamus to the auditory cortex in SZ patients, resulting in auditory perception without external auditory stimuli. Our results provide insights into the neural mechanisms underlying AVHs in SZ. This thalamic-auditory cortical-hippocampal dysconnectivity may also serve as a diagnostic biomarker of AVHs in SZ and a therapeutic target based on direct in vivo evidence.

The differential genetic signatures related to climatic landscapes for jaguars and pumas on a continental scale

Modern and paleoclimate changes may have altered species dynamics by shifting species’niche suitability over space and time.We analyze whether the current genetic structure and isolation of the two large American felids,jaguar(Panthera onca)and puma(Puma concolor),are mediated by changes in climatic suitability and connection routes over modern and paleoclimatic landscapes.We estimate species distribution under 5 climatic landscapes(modern,Holocene,last maximum glaciations[LMG],average suitability,and climatic instability)and correlate them with individuals’genetic isolation through causal modeling on a resemblance matrix.Both species exhibit genetic isolation patterns correlated with LMG climatic suitability,suggesting that these areas may have worked as“allele refuges.”However,the jaguar showed higher vulnerability to climate changes,responding to modern climatic suitability and connection routes,whereas the puma showed a continuous and gradual transition of genetic variation.Despite differential responsiveness to climate change,both species are subjected to the climatic effects on genetic configuration,which may make them susceptible to future climatic changes,since these are progressing faster and with higher intensity than changes in the paleoclimate.Thus,the effects of climatic changes should be considered in the design of conservation strategies to ensure evolutionary and demographic processes mediated by gene flow for both species.

Instruction Cues Increase Brain Network Complexity During Movement Preparation

Instruction cues are widely employed for research on neural mechanisms during movement preparation.However,their influence on brain connectivity during movement has not received much attention.Herein,15 healthy subjects completed two experimental tasks including either instructed or voluntary movements;meanwhile electroencephalogram(EEG)data were synchronously recorded.Based on source analysis and related literature,six movement-related brain regions were selected,including the left/right supplementary motor area(SMA),left/right inferior frontal gyrus(iFg),and left/right postcentral gyrus(pCg).After assuming 10 a priori models of regional brain connectivity,we evaluated the optimal connectivity model between brain regions for the two scenarios using the dynamic causality model(DCM).During voluntary movement,the movement originated in the SMA,passed through the iFg of the prefrontal lobe,and then returned to the main sensory cortex of the pCg.In the instructed movement,the movement originated in the iFg,and then was transmitted to the pCg and the SMA,as well as from the pCg to the SMA.In contrast to the preparation process of voluntary movement,there were long-range information interactions between the iFg and pCg.Further,almost the same brain regions were active during movement preparation under both voluntary and instructed movement tasks,which evidences certain similarities in dynamic brain connectivity,that is,the brain has direct connections between the bilateral SMA,bilateral pCg,and bilateral SMA,indicating that the both brain hemispheres work together during the movement preparation phase.The results suggest that the network during the preparation process of instructed movements is more complex than voluntary movements.