Causal explanation and scientific realism

It is widely believed that many of the competing accounts of scientific explanation have ramifications which are relevant to the scientific realism debate. I claim that the two issues are orthogonal. For definiteness, I consider Cartwright's argument that causal explanations secure belief in theoretical entities. In Section I, van Fraassen's anti-realism is reviewed; I argue that this anti-realism is, prima facie, consistent with a causal account of explanation. Section II reviews Cartwright's arguments. In Section III, it is argued that causal explanations do not license the sort of inferences to theoretical entities that would embarass the anti-realist. Section IV examines the epistemic commitments involved in accepting a causal explanation. Section V presents my conclusions: contra Cartwright, the anti-realist may incorporate a causal account of explanation into his vision of science in an entirely natural way.     

Causal explanation in the face of contradiction

Previous work has shown that predictions can be mediated by mechanistic beliefs. The present study shows that such mediation only occurs in the face of contradictory, and not corroborative, evidence. In four experiments, we presented participants with causal statements describing a common-cause structure (E₁←C→E₂). Then we informed them of the states of C and E₁ and asked them to judge the likelihood of E₂. In Experiments 1 and 2, we manipulated whether the mechanisms supporting the two effects were the same or different, and whether the evidence presented confirmed or contradicted the participants’ expectations. The relation between the mechanisms only influenced predictions when evidence contradicted the expectations, but not when it was consistent. In Experiments 3 and 4, we used a common-cause structure with identical mechanisms. We manipulated the order in which predictions were made. When confirmatory predictions were made before contradictory predictions, mechanistic modulation was not observed in the confirmatory case. In contrast, the modulation was found when confirmatory predictions were made after contradictory ones. The results support the contradiction hypothesis that causal structure is revised during prediction, but only in the face of unexpected evidence.     

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.     

Causal explanation improves judgment under uncertainty,but rarely in a Bayesian way

Three studies reexamined the claim that clarifying the causal origin of key statistics can increase normative performance on Bayesian problems involving judgment under uncertainty. Experiments 1 and 2 found that causal explanation did not increase the rate of normative solutions. However, certain types of causal explanation did lead to a reduction in the magnitude of errors in probability estimation. This effect was most pronounced when problem statistics were expressed in percentage formats. Experiment 3 used process-tracing methods to examine the impact of causal explanation of false positives on solution strategies. Changes in probability estimation following causal explanation were the result of a mixture of individual reasoning strategies, including non-Bayesian mechanisms, such as increased attention to explained statistics and approximations of subcomponents of Bayes’ rule. The results show that although causal explanation of statistics can affect the way that a problem is mentally represented, this does not necessarily lead to an increased rate of normative responding.     

Functional explanation and the function of explanation

Teleological explanations (TEs) account for the existence or properties of an entity in terms of a function: we have hearts because they pump blood, and telephones for communication. While many teleological explanations seem appropriate, others are clearly not warranted--for example, that rain exists for plants to grow. Five experiments explore the theoretical commitments that underlie teleological explanations. With the analysis of [Wright, L. (1976). Teleological Explanations. Berkeley, CA: University of California Press] from philosophy as a point of departure, we examine in Experiment 1 whether teleological explanations are interpreted causally, and confirm that TEs are only accepted when the function invoked in the explanation played a causal role in bringing about what is being explained. However, we also find that playing a causal role is not sufficient for all participants to accept TEs. Experiment 2 shows that this is not because participants fail to appreciate the causal structure of the scenarios used as stimuli. In Experiments 3-5 we show that the additional requirement for TE acceptance is that the process by which the function played a causal role must be general in the sense of conforming to a predictable pattern. These findings motivate a proposal, Explanation for Export, which suggests that a psychological function of explanation is to highlight information likely to subserve future prediction and intervention. We relate our proposal to normative accounts of explanation from philosophy of science, as well as to claims from psychology and artificial intelligence.     

Causal Exclusion and Causal Autonomism

The causal exclusion problem is often considered as one of the major difficulties for which non-reductive physicalists have no easy solution to offer. Some non-reductive physicalists address this problem by arguing that mental properties are to some extent causally autonomous. If this is the case, then mental properties will not be causally excluded by their physical realizers because causation, in general, is a relation between properties of the same level. In this paper, I argue that the response from causal autonomy cannot be successful for two reasons. First, it does not offer a satisfactory explanation for how mental particulars can have causal efficacy in a non-reductive physicalist framework. Second, the causal considerations underpinning this response do not really support the conclusion that mental properties are causally autonomous.     

Causal Exclusion and Causal Bayes Nets

In this paper I reconstruct and evaluate the validity of two versions of causal exclusion arguments within the theory of causal Bayes nets. I argue that supervenience relations formally behave like causal relations. If this is correct, then it turns out that both versions of the exclusion argument are valid when assuming the causal Markov condition and the causal minimality condition. I also investigate some consequences for the recent discussion of causal exclusion arguments in the light of an interventionist theory of causation such as Woodward's ( 2003 ) and discuss a possible objection to my causal Bayes net reconstruction.     

Expertise and the explanation effect

The effect of professional experience on the “explanation effect”, i.e., generation of an explanation for an event occurrence increasing the judged likelihood of the event, is investigated in a risk assessment (financial auditing) context. An explanation effect was predicted for inexperienced auditors (auditing students); however, audit judgment experience was predicted to mediate, or eliminate, any explanation effect. Two competing hypotheses for the origin of the effect, the causal construction and recall-availability hypotheses, are tested given the presence of antecedent conditions for, and against, the explanation events. Audit risk judgments were provided by 58 novice and 42 experienced auditors. Written explanation for occurrence of the target event resulted in the explanation effect for novice subjects, both for specifie event and aggregate risk assessments. The pattern of results supported the recallavailability over the causal construction hypothesis. The judgments of the experienced auditors, however, did not indicate any explanation effect.     

Functional explanation,consequence explanation,and Marxism

I argued in Karl Marx's Theory of History that the central claims of historical materialism are functional explanations, and I said that functional explanations are consequence explanations, ones, that is, in which something is explained by its propensity to have a certain kind of effect. I also claimed that the theory of chance variation and natural selection sustains functional explanations, and hence consequence explanations, of organismic equipment. In Section I I defend the thesis that historical materialism offers functional or consequence explanations, and I reject Jon Elster's contention that game theory can, and should, assume a central role in the Marxist theory of society. In Section II I contrast functional and consequence explanation, thereby revising the position of Karl Marx's Theory of History, and I question whether evolutionary biology supports functional explanations. Section III is a critique of Elster's views on functional explanation, and Sections IV and V defend consequence explanation against metaphysical and epistemological doubts. A concluding section summarizes my present understanding of the status of historical materialist explanations.     

Causal Closure,Causal Exclusion,and Supervenience Physicalism

This article considers the recent defense of the supervenience approach to physicalism due to Jaegwon Kim. Kim argues that supervenience supports physical causal closure, and that causal closure supports physicalism – indeed, a kind of reductive physicalism – and thus that supervenience suffices for physicalism. After laying out Kim's argument, I ask whether its success would truly vindicate the role of supervenience in defining physicalist positions. I argue that it would not, and that insofar as Kim's defense of supervenience physicalism succeeds, it does so by showing that supervenience physicalism is not a unique, nonredundant way to be a physicalist.     

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.     

Ambiguity and explanation

This paper presents evidence that ‘because’ is importantly ambiguous between two closely related senses covering what are usually called causal explanations, on the one hand, and grounding or metaphysical explanations, on the other hand. To this end, it introduces the lexical categories of monosemy, polysemy and homonymy; describes a test for polysemy; and discusses the results of the test when applied to ‘because’. It also shows how to understand so-called hybrid explanations in light of the semantic facts established by the analysis.     

Supervenience and explanation

This paper explores the explanatory adequacy of lower-level theories when their higher-level counterparts are irreducible. If some state or entity described by a high-level theory supervenes upon and is realized in events, entities, etc. described by the relevant lower-level theory, does the latter fully explain the higher-level event even if the higher-level theory is irreducible? While the autonomy of the special sciences and the success of various eliminativist programs depends in large part on how we answer this question, neither the affirmative or negative answer has been defended in detail. I argue, contra Putnam and others, that certain facts about causation and explanation show that such lower-level theories do explain. I also argue, however, that there may be important questions about counterfactuals and laws that such explanations cannot answer, thereby showing their partial inadequacy. I defend the latter claim against criticisms based on eliminativism about higher-level explanations and sketch a number of empirical conditions that lower-level explanations would have to meet to fully explain higher-level events.     

Reason explanation and the second-person perspective

On a widely held view, the canonical way to make sense of intentional actions is to invoke the agent's ‘motivating reasons’, where the claim that X did A for some ‘motivating reason’ is taken to be neutral on whether X had a normative reason to do A. In this paper, I explore a challenge to this view, drawing on Anscombe's ‘second-personal’ approach to the nature of action explanation.     

Stable Causal Relationships Are Better Causal Relationships

We report three experiments investigating whether people's judgments about causal relationships are sensitive to the robustness or stability of such relationships across a range of background circumstances. In Experiment 1, we demonstrate that people are more willing to endorse causal and explanatory claims based on stable (as opposed to unstable) relationships, even when the overall causal strength of the relationship is held constant. In Experiment 2, we show that this effect is not driven by a causal generalization's actual scope of application. In Experiment 3, we offer evidence that stable causal relationships may be seen as better guides to action. Collectively, these experiments document a previously underappreciated factor that shapes people's causal reasoning: the stability of the causal relationship.     

幸福悖论及其最新解释

横断研究认为收入和幸福感正相关; 长时时间序列(通常指10年及以上)研究认为二者无关, 这种矛盾的结论即构成了幸福悖论。传统上解释幸福悖论主要从理论着手, 如定点理论、适应理论、相对效用理论等。近年来, 学者们开始从“忽略变量”的角度解释, 文章分别介绍了收入不平等、受教育水平、社会资本、个人主义–集体主义、婚姻状态五种忽略变量。忽略变量对幸福感的消极效应抵消了收入对幸福感的积极效应, 导致了幸福悖论现象的发生。