An information integration account for causal generalizations in uncertain causal networks

In reasoning about common cause networks, given that a cause generates an effect, people often need to infer how likely the cause generate another effect. This causal generalization question has not systematically been investigated in previous research. We propose the information integration account for causal generalizations in uncertain casual networks with dichotomized continuous variables. It predicts that causal generalization is the joint function of conditional probabilities of causal links and cause strength indicated by the proportion of present collateral effects. Two experiments investigated causal generalizations in uncertain causal networks with and without probability distributions, respectively. It was found that in the presence of probability distributions there was the joint effect of conditional probability and cause strength on causal generalization; in the absence of probability distributions causal generalization depend only on cause strength. The overall response pattern favors the information integration account over the other alternative accounts.

Comment on "Estimating causal networks in biosphere–atmosphere interaction with the PCMCI approach"

This is a comment on "Estimating causal networks in biosphere–atmosphere interaction with the PCMCI approach" by Krich et al., Biogeosciences, 17, 1033–1061, 2020, which gives a good introduction to causal discovery, but confines the scope by investigating the outcome of a single algorithm. In this comment, we argue that the outputs of causal discovery algorithms should not usually be considered as end results but starting points and hypothesis for further study. We illustrate how not only different algorithms, but also different initial states and prior information of possible causal model structures, affect the outcome. We demonstrate how to incorporate expert domain knowledge with causal structure discovery and how to detect and take into account overfitting and concept drift.

Perceived Causal Problem Networks: Reliability, Central Problems, and Clinical Utility for Depression

Personalized case conceptualization is often regarded as a prerequisite for treatment success in psychotherapy for patients with comorbidity. This article presents Perceived Causal Networks, a novel method in which patients rate perceived causal relations among behavioral and emotional problems. First, 231 respondents screening positive for depression completed an online Perceived Causal Networks questionnaire. Median completion time (including repeat items to assess immediate test–retest reliability) was 22.7 minutes, and centrality measures showed excellent immediate test–retest reliability. Networks were highly idiosyncratic, but worrying and ruminating were the most central items for a third of respondents. Second, 50 psychotherapists rated the clinical utility of Perceived Causal Networks visualizations. Ninety-six percent rated the networks as clinically useful, and the information in the individual visualizations was judged to contain 47% of the information typically collected during a psychotherapy assessment phase. Future studies should individualize networks further and evaluate the validity of perceived causal relations.

Causal influence in operational probabilistic theories

We study the relation of causal influence between input systems of a reversible evolution and its output systems, in the context of operational probabilistic theories. We analyse two different definitions that are borrowed from the literature on quantum theory—where they are equivalent. One is the notion based on signalling, and the other one is the notion used to define the neighbourhood of a cell in a quantum cellular automaton. The latter definition, that we adopt in the general scenario, turns out to be strictly weaker than the former: it is possible for a system to have causal influence on another one without signalling to it. Remarkably, the counterexample comes from classical theory, where the proposed notion of causal influence determines a redefinition of the neighbourhood of a cell in cellular automata. We stress that, according to our definition, it is impossible anyway to have causal influence in the absence of an interaction, e.g. in a Bell-like scenario. We study various conditions for causal influence, and introduce the feature that we call no interaction without disturbance, under which we prove that signalling and causal influence coincide. The proposed definition has interesting consequences on the analysis of causal networks, and leads to a revision of the notion of neighbourhood for classical cellular automata, clarifying a puzzle regarding their quantisation that apparently makes the neighbourhood larger than the original one.

Causal relationships between particulate matter and COVID-19 cases in USA cities

<p>Recent studies have shown that increased outdoor concentrations of particulate matter (PM) enhances the transmission of the novel Coronavirus (COVID-19). Although the viability of this causal relationship has been established indoors, outdoor correlations are contested based on potential confounding effects, such as urban mobility. Testing the hypothesis of PM-assisted airborne viral transmission is important to support the decision-making process and mitigation of future airborne epidemics. In a recent study we have shown that Granger causality analysis supports a causal relationship between outdoor PM concentrations and COVID-19 new cases. In this study, we aim to further explore this causal link by considering urban mobility as a common driver and a mediator in a set of causal networks based on lagged multivariate linear regressions. Causal networks are graphical models designed to help distinguishing and quantifying correlation and causation relationships. We quantify the strength at which PM increases COVID-19 new cases directly and the strength of urban mobility as a driver of both PM and COVID-19 new cases. We also quantify the effect of COVID-19 new cases in urban mobility that causes the PM concentration. We employ a dataset of daily air quality measurements in 52 cities in the United States of America (USA) considering PM concentrations in two particle size ranges, smaller than 2.5 μm (PM2.5), and between 10 and 2.5 μm (PMC). PMC is related to soil dust resuspension in most cities. So, we used the PMC as an urban mobility proxy. We also employ carbon monoxide (CO) along with the Apple dataset of IPhone users mobility which shows the relative volume of satellite navigation requests by city in the USA.</p>

A Causal Network-Based Risk Matrix Model Applicable to Shield TBM Tunneling Projects

The present study compares and analyzes three risk analysis models that are applicable to shield tunnel boring machine (TBM) tunneling, and thus proposes an improved risk matrix model based on the causal networks applicable to sustainable tunnel projects. The advantages and disadvantages of three risk analysis models are compared, and causal networks are structured by analyzing the causal relationship between risk factors and risk events. Based on the comparison and analysis results, the causal network-based risk matrix model (CN-Matrix model), which complements the disadvantages and exploits the advantages of the three existing models, is proposed in this paper. Furthermore, this study suggests a means of modifying the weighting scores in the estimation of the risk score, which permits the CN-Matrix model to determine the risk level more reasonably. Thus, the improved CN-Matrix model is more reliable and robust compared to the three existing models.

Emotions as Overlapping Causal Networks of Emotion Components: Implications and Methodological Approaches

A widespread perspective describes emotions as distinct categories bridged by fuzzy boundaries, indicating that emotions are distinct and dimensional at the same time. Theoretical and methodological approaches to this perspective still need further development. We conceptualize emotions as overlapping networks of causal relationships between emotion components—networks representing distinct emotions share components with and relate to each other. To investigate this conceptualization, we introduce network analysis to emotion research and apply it to the reanalysis of a data set on multiple positive emotions. Specifically, we describe the estimation of networks from data, and the detection of overlapping communities of nodes in these networks. The network perspective has implications for the understanding of distinct emotions, their co-occurrence, and their measurement.