Causal reasoning and the diagnostic process

Background: Causal reasoning as a way to make a diagnosis seems convincing. Modern medicine depends on the search for causes of disease and it seems fair to assert that such knowledge is employed in diagnosis. Causal reasoning as it has been presented neglects to some extent the conception of multifactorial disease causes. Goal: The purpose of this paper is to analyze aspects of causation relevant for discussing causal reasoning in a diagnostic context. Procedures: The analysis will discuss different conceptions of causal reasoning in medical diagnosis, discriminating primarily between narrow causal diagnosis and more thorough causal explanation. The theory of causes as non-redundant factors in effective causal complexes is used as an analytical background. Causal explanations are performed according to different causal models. Such models of diagnosis are assumptions concerning structure and mechanisms, which cannot be directly or immediately observed. Conceptions and results of causal search strategies differ, according to the focus of the searcher. Causal reasoning is also seen in diagnosis in a more extensive meaning: the pin-pointing of factors responsible for the condition of the patient at any time during the course of disease. Conclusion: Causal reasoning and diagnosis go well in hand, especially if both concepts are widened. The theory of causes as non-redundant components in effective causal complexes, modulated by what is referred to as the stop problem and causal fields, is valuable for explaining the many aspects of causal reasoning in medical diagnosis.     

Causal modeling with the TETRAD program

Drawing substantive conclusions from linear causal models that perform acceptably on statistical tests is unreasonable if it is not known how alternatives fare on these same tests. We describe a computer program, TETRAD, that helps to search rapidly for plausible alternatives to a given causal structure. The program is based on principles from statistics, graph theory, philosophy of science, and artificial intelligence. We describe these principles, discuss how TETRAD employs them, and argue that these principles make TETRAD an effective tool. Finally, we illustrate TETRAD's effectiveness by applying it to a multiple indicator model of Political and Industrial development. A pilot version of the TETRAD program is described in this paper. The current version is described in our forthcoming Discovering Causal Structure: Artificial Intelligence for Statistical Modeling.     

Causal beliefs and empirical evidence

Causal beliefs often facilitate decision making. However, strong causal beliefs can also lead to neglect of relevant empirical evidence causing errors in risky decision making (e.g., medical, financial). We investigated the impact of pre-training and post-experience on the evaluation of empirical evidence in a two-alternative medical diagnostic task. Participants actively searched for information about two patients on the basis of four available cues. The first experiment indicated that pre-training can weaken the strong influence of causal beliefs reducing neglect of empirical evidence. The second experiment demonstrated that increasing amounts of empirical evidence can improve people's ability to decide in favor of a correct diagnosis. The current research converges with other recent work to clarify key mechanisms and boundary conditions shaping the influence of causal beliefs and empirical evidence in decisions and causal judgments.     

Causal essentialism and mereological monism

Several philosophers have recently defended Causal Essentialism—the view that every property confers causal powers, and whatever powers it confers, it confers essentially. I argue that on the face of it, Causal Essentialism implies a form of Monism, and in particular, the thesis I call ‘Mereological Monism’: that there is some concretum that is a part of every concretum. However, there are three escape routes, three views which are such that if one of them is true, Causal Essentialism does not imply any form of Monism at all. I survey the costs associated with taking these escape routes along with the costs associated with accepting Mereological Monism.     

Causal and Metaphysical Necessity

Any property has two sorts of causal features: "forward-looking" ones, having to do with what its instantiation can contribute to causing, and ldquo;backward-looking" ones, having to do with how its instantiation can be caused. Such features of a property are essential to it, and properties sharing all of their causal features are identical. Causal necessity is thus a special case of metaphysical necessity. Appeals to imaginability have no more force against this view than they do against the Kripkean view that statements like "Gold is an element" are metaphysically necessary.     

Causal Responsibility and Rationing in Medicine

The article addresses the issue of rationing health care services, a topic currently being hotly debated in many countries. The author argues that the aspect of causal responsibility ought to play a decisive role in the allocation of limited medical resources. Starting out from Ronald Dworkin's distinction between option luck and brute luck, the appropriate and meaningful uses of the term causal responsibility are clarified first. A discussion of the conditions which might justify giving lower priority to patients whose illnesses are the result of unhealthy behavior, like e.g. alcohol abuse, follows. Causal responsibility is then viewed in the context of private health insurance and the club model of organ donation. It is argued that individuals themselves are basically responsible for their decisions regarding insurance coverage and membership in organ donors' clubs. Causal responsibility is shown to be a more suitable criterion for rationing scarce medical resources than other criteria which might alternatively be considered, such as patients' age.     

Relevant Alternatives,Perceptual Knowledge and Discrimination

This paper examines the relationship between perceptual knowledge and discrimination in the light of the so‐called ‘relevant alternatives’ intuition. It begins by outlining an intuitive relevant alternatives account of perceptual knowledge which incorporates the insight that there is a close connection between perceptual knowledge and the possession of relevant discriminatory abilities. It is argued, however, that in order to resolve certain problems that face this view, it is essential to recognise an important distinction between favouring and discriminating epistemic support that is often overlooked in the literature. This distinction complicates the story regarding how an alternative becomes relevant, and in doing so weakens the connection between perceptual knowledge and discrimination. The theory that results, however—what I term a ‘two‐tiered’ relevant alternatives theory of perceptual knowledge—accommodates many of our intuitions about perceptual knowledge and so avoids the revisionism of some recent proposals in the epistemological literature.     

Causal models frame interpretation of mathematical equations

We offer evidence that people can construe mathematical relations as causal. The studies show that people can select the causal versions of equations and that their selections predict both what they consider most understandable and how they expect variables to influence one another. When asked to write down equations, people have a strong preference for the version that matches their causal model. Causal models serve to structure equations by determining the preferred order of variables: Causes should be on one side of an equality, and a single effect should appear on the other.     

Causal Impotence and Evolutionary Influence: Epistemological Challenges for Non-Naturalism

Two epistemological critiques of non-naturalism are not always carefully distinguished. According to the Causal Objection, the fact that moral properties cannot cause our moral beliefs implies that it would be a coincidence if many of them were true. According to the Evolutionary Objection, the fact that evolutionary pressures have influenced our moral beliefs implies a similar coincidence. After distinguishing these epistemological critiques, I provide an extensive defense of the Causal Objection that also strengthens the Evolutionary Objection. In particular, I formulate a “Master Causal Objection” featuring the controversial premise that non-naturalism can provide no adequate explanation for moral knowledge. I defend this premise by first narrowing down the range of candidate explanations to conceptual, constitutive, and evolutionary explanations, and then considering and eliminating each of these in turn.  My discussion of evolutionary explanations suggests that non-naturalists must refute the Causal Objection in order to refute the Evolutionary Objection.     

Causal Cognitive Architecture 3: A solution to the binding problem

The binding problem is considered in terms of how a brain-inspired cognitive system can recognize multiple sensory features from an object which may be among many objects, process those features individually and then bind the multiple features to the object they belong to. The Causal Cognitive Architecture 3 (CCA3) is a brain-inspired cognitive architecture using a multi-dimensional navigation map as its basic store of information, and capable of pre-causal as well as fully causal behavior. Objects within an input sensory scene are segmented, and sensory features (e.g., visual, auditory, etc.) of each segmented object are spatially mapped onto a variety of navigation maps. It is shown that to provide efficient, flexible, causal solutions to real-world problems, it is not sufficient to bind space (i.e., objects spatially) but it is necessary to also bind time (i.e., change and rate of change of objects within a sensory scene). The CCA3 binds both space and time onto a navigation map as physical features, and is better able to function in real-world environments. As the CCA3 is brain-inspired, the Causal Cognitive Architecture can help to better hypothesize and understand biological mammalian brain function, including solutions to the binding problem. The CCA3 architecture allows it to work in different knowledge domains, possess continual lifelong learning, and demonstrate reasonable explainability.     

Causal Theories of Knowledge1

Causal theories of knowledge require some causal connection between belief and the conditions whose existence make that belief true. Lacking this connection, the belief may be true, it may be altogether reasonable, but it is not knowledge.     

The Effects of Gender and Measurement Strategy on Causal Attributions for Infertility

Investigators of causal attributions for threatening events have typically studied either male or female samples and have interchangeably used two methods of assessing attributions. To examine the effect of gender and measurement strategy on causal attributions, we interviewed 31 men and 33 women with impaired fertility. Causal attributions were measured using open-ended questions, as well as by asking participants to rate the influence of five specific causes. The results of a multitrait-multimethod matrix revealed only modest convergence between measurement methods. As predicted, both method and gender influenced causal attributions. Women were more likely to attribute the infertility to their behavior. Causal attributions were related to psychological symptoms, but differentially depending on how attributions were measured.     

Causal attributions of shy subjects

Causal attributions of shy students (N = 36) were compared with those of a comparison group of students (N = 36) in 10 situations. The major hypothesis was that shy students would have a self-defeating pattern of attributions as compared to the norm group, but only in specific situations. Significant differences between the two groups emerged when explaining outcomes of situations considered to be problematic for shy individuals. Causal attributions may reflect realistic and situation-specific self-perceptions.     

The Structure of Causal Evidence Based on Eliminative Induction

It is argued that in deterministic contexts evidence for causal relations states whether a boundary condition makes a difference or not to a phenomenon. In order to substantiate the analysis, I show that this difference/indifference making is the basic type of evidence required for eliminative induction in the tradition of Francis Bacon and John Stuart Mill. To this purpose, an account of eliminative induction is proposed with two distinguishing features: it includes a method to establish the causal irrelevance of boundary conditions by means of indifference making, which is called strict method of agreement, and it introduces the notion of a background against which causal statements are evaluated. Causal statements thus become three-place-relations postulating the relevance or irrelevance of a circumstance C to the examined phenomenon P with respect to a background B of further conditions. To underline the importance of evidence in terms of difference/indifference making, I sketch two areas, in which eliminative induction is extensively used in natural and engineering sciences. One concerns exploratory experiments, the other engineering design methods. Given that a method is discussed that has been used for centuries, I make no claims to novelty in this paper, but hope that the combined discussion of several topics that are still somewhat underrepresented in the philosophy of science literature is of some merit.     

Causal learning is collaborative: Examining explanation and exploration in social contexts

Causal learning in childhood is a dynamic and collaborative process of explanation and exploration within complex physical and social environments. Understanding how children learn causal knowledge requires examining how they update beliefs about the world given novel information and studying the processes by which children learn in collaboration with caregivers, educators, and peers. The objective of this article is to review evidence for how children learn causal knowledge by explaining and exploring in collaboration with others. We review three examples of causal learning in social contexts, which elucidate how interaction with others influences causal learning. First, we consider children’s explanation-seeking behaviors in the form of “why” questions. Second, we examine parents’ elaboration of meaning about causal relations. Finally, we consider parents’ interactive styles with children during free play, which constrains how children explore. We propose that the best way to understand children’s causal learning in social context is to combine results from laboratory and natural interactive informal learning environments.     

Précis of <Emphasis Type="Italic">Doing without Concepts</Emphasis>

Semantic Minimalists make a proprietary claim to explaining the possibility of utterances sharing content across contexts. Further, they claim that an inability to explain shared content dooms varieties of Contextualism. In what follows, I argue that there are a series of barriers to explaining shared content for the Minimalist, only some of which the Contextualist also faces, including: (i) how the type-identity of utterances is established, (ii) what counts as repetition of type-identical utterances, (iii) how it can be determined whether semantically minimal content has been repeated, and (iv) what the nature of such content is.     

发展情境论——一种新的发展系统理论

发展情境论（Developmental Contextualism）是发展系统理论体系中的一种具有代表性的理论。该理论认为人的发展是通过发展中的个体与其所处情境间的持续交互作用实现的,个体的发展变化过程在本质上是或然渐成性的。该理论不仅更系统地概括了影响人发展的时间和空间因素,而且在交互作用观点的基础上提出了循环影响的研究取向。同时,发展情境论认为人的可塑性特点为干预个体的发展提供了可能,并进一步提出了干预的思路,即通过建立和调整个体与所处情境的拟合优度模型来实现个体和所处情境的积极发展。