Ramsey's Foundations Extended to Desirabilities

In his Truth and Probability (1926), Frank Ramsey provides foundations for measures of degrees of belief in propositions and preferences for worlds. Nonquantitative conditions on preferences for worlds, and gambles for worlds and certain near-worlds, are formulated which he says insure that a subject's preferences for worlds are represented by numbers, world values. Numbers, for his degrees of belief in propositions, probabilities, are then defined in terms of his world values. Ramsey does not also propose definitions of desirabilities for propositions, though he is in a position to do this. Given his measures for probabilities of propositions and values of worlds, he can frame natural definitions for both evidential and causal desirabilities that would measure respectively the welcomeness of propositions as items of news, and as facts. His theory is neutral with respect to the evidential/causal division. In the present paper, as Ramsey's foundations are explained, several problems and limitations are noted. Their distinctive virtue – their evidential/causal neutrality – is demonstrated. Comparisons are made with other foundational schemes, and a perspective is recommended from which nonquantitative foundations are not the be all for quantitative theories of ideal preferences and credences.     

基于贝叶斯网络的操作风险建模

操作风险是指由不完善的或有问题的内部程序、人员及系统或外部事件所造成损失的风险。由于操纵风险具有构成更复杂、涉及诸多复杂因素、难以结构化、缺少历史数据等特点,因此难于建模与度量。贝叶斯网络是基于贝叶斯决策理论的因果建模技术。它能够用于建立操作风险度量系统并作为操作风险度量的基础。本文首先回顾了操作风险的相关内容。接下来介绍贝叶斯网络的概念、特点,以及贝叶斯网络模型的数学描述。通过实例演示了贝叶斯网络在银行操作风险方面的建模与应用。最后给出了一个操作风险管理的贝叶斯网络模型的框架。     

Estimating efficacy in a proposed randomized trial with initial and later non-compliance

Summary.  A controversial topic in obstetrics is the effect of walking on the probability of Caesarean section among women in labour. A major reason for the controversy is the presence of non-compliance that complicates the estimation of efficacy, the effect of treatment received on outcome. The intent-to-treat method does not estimate efficacy, and estimates of efficacy that are based directly on treatment received may be biased because they are not protected by randomization. However, when non-compliance occurs immediately after randomization, the use of a potential outcomes model with reasonable assumptions has made it possible to estimate efficacy and still to retain the benefits of randomization to avoid selection bias. In this obstetrics application, non-compliance occurs initially and later in one arm. Consequently some parameters cannot be uniquely estimated without making strong assumptions. This difficulty is circumvented by a new study design involving an additional randomization group and a novel potential outcomes model (principal stratification).     

A hierarchical modelling approach to analysing longitudinal data with drop-out and non-compliance, with application to an equivalence trial in paediatric acquired immune deficiency syndrome

Longitudinal clinical trials with long follow-up periods almost invariably suffer from a loss to follow-up and non-compliance with the assigned therapy. An example is protocol 128 of the AIDS Clinical Trials Group, a 5-year equivalency trial comparing reduced dose zidovudine with the standard dose for treatment of paediatric acquired immune deficiency syndrome patients. This study compared responses to treatment by using both clinical and cognitive outcomes. The cognitive outcomes are of particular interest because the effects of human immunodeficiency virus infection of the central nervous system can be more acute in children than in adults. We formulate and apply a Bayesian hierarchical model to estimate both the intent-to-treat effect and the average causal effect of reducing the prescribed dose of zidovudine by 50%. The intent-to-treat effect quantifies the causal effect of assigning the lower dose, whereas the average causal effect represents the causal effect of actually taking the lower dose. We adopt a potential outcomes framework where, for each individual, we assume the existence of a different potential outcomes process at each level of time spent on treatment. The joint distribution of the potential outcomes and the time spent on assigned treatment is formulated using a hierarchical model: the potential outcomes distribution is given at the first level, and dependence between the outcomes and time on treatment is specified at the second level by linking the time on treatment to subject-specific effects that characterize the potential outcomes processes. Several distributional and structural assumptions are used to identify the model from observed data, and these are described in detail. A detailed analysis of AIDS Clinical Trials Group protocol 128 is given; inference about both the intent-to-treat effect and average causal effect indicate a high probability of dose equivalence with respect to cognitive functioning.     

Is Early Smoking a Causal Risk Factor for Later Cognitive Impairment? A 20-year Prospective Study with Time-varying Propensity Score Matching Based on Random Intercept and Slope

Although epidemiological studies support an association between smoking and cognitive impairment, existing data do not answer the question of whether this association is causal or arises from covariates. In this paper, we investigate smoking status, assessed from adolescence to adulthood, and subsequent cognitive problems in a large representative sample of youths. To analyze this data, we propose a method for causal effects using full matching based on the subject-specific random intercept and slope of the propensity scores. The findings suggest that earlier smoking is not a causal factor for later cognitive problem (odds ratio = 1.64, 95% CI: 0.97–2.80, p = 0.06).     

A simpler approach for mediation analysis for dichotomous mediators in logistic regression

Mediation is a hypothesized causal chain among three variables. Mediation analysis for continuous response variables is well developed in the literature, and it can be shown that the indirect effect is equal to the total effect minus the direct effect. However, mediation analysis for categorical responses is still not fully developed. The purpose of this article is to propose a simpler method of analysing the mediation effect among three variables when the dependent and mediator variables are both dichotomous. We propose using the latent variable technique which in turn will adjust for the necessary condition that indirect effect is equal to the total effect minus the direct effect. An intensive simulation study is conducted to compare the proposed method with other methods in the literature. Our theoretical derivation and simulation study show that the proposed approach is simpler to use and at least as good as other approaches provided in the literature. We illustrate our approach to test for the potential mediators on the relationship between depression and obesity among children and adolescents compared to the method in Winship and Mare using National children health survey data 2011–2012.     

反倾销中因果关系的确定

倾销和损害之间存在因果关系是实施反倾销的一个基本条件。本文就反倾销中确定因果关系的标准和方法进行了详细探讨。在确定因果关系时,有“主要原因”和“原因之一”两类标准,但只有前者才符合WTO的基本精神。为保证倾销和损害之间因果关系的真实性,应审查肯定性和否定性两个方面的证据。     

A Test of Alternative Measures of Disconfirmation*

Disconfirmation has been widely used in a number of research traditions, however there are many different operationalizations of this construct. Little research has investigated the relative effectiveness of these various methods. The research reported here examines five operationalizations of disconfirmation and their effect on satisfaction. These tests are carried out using two different comparison standards in two different settings. The results indicate some methods are better in certain situations and are inappropriate in others. Implications for both practical and theoretical research are discussed.     

Experiential and Informational Knowledge,Architectural Marketing Capabilities,and the Adaptive Performance of Export Ventures: A Cross‐National Study

Knowledge‐based view (KBV) theory posits that the acquisition and use of relevant knowledge is key to understanding organizational performance. However, there is relatively little empirical evidence to support or refute several important propositions underlying KBV theory explanations of organizational performance. In particular, the extant literature has focused on individual technical and scientific components of the knowledge bases of firms in dynamic industries, and largely ignored both different levels of informational and experiential knowledge relevant to the market environment, and the increasingly important context of exporting. Our study addresses these knowledge gaps by developing a framework for export venture knowledge management and empirically examining relationships between different types of individual‐level and organizational‐level knowledge relevant to the market environment, architectural marketing capabilities, and the adaptive performance of export ventures. Using primary data collected in the United Kingdom and China, our study indicates that export ventures' organizational‐level experiential and informational knowledge, and individual‐level experiential knowledge relevant to the market environment, is positively associated with export ventures' architectural marketing capabilities, which are in turn associated with the adaptive performance of export ventures.     

Why are mediation effects so small?

Identifying mediators in variable chains as part of a causal mediation analysis can shed light on issues of causation, assessment, and intervention. However, coefficients and effect sizes in a causal mediation analysis are nearly always small. This can lead those less familiar with the approach to reject the results of causal mediation analysis. The current paper highlights five factors that contribute to small path coefficients in mediation research: loss of information when measuring relationships across time, controlling for prior levels of a predicted variable, adding control variables to the analysis, ignoring measurement error in one’s variables, and using multiple mediators. It is argued that these issues are best handled by increasing the statistical power of the analysis, identifying the optimal temporal interval between variables, using bootstrapped confidence intervals to analyze the results, and finding alternate ways of assessing the meaningfulness of the indirect effect.