The Rationale of Variation in Methodological and Evidential Pluralism

Q4 Arts and Humanities
F. Russo
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引用次数: 17

Abstract

Causal analysis in the social sciences takes advantage of a variety of methods and of a multi-fold source of information and evidence. The first developments of quantitative causal analysis in the social sciences are due to Quetelet (1869) and Durkheim (1895 and 1897) in demography and sociology respectively. Significant improvements are due to Blalock (1964) and Duncan (1975). Since then causal analysis has shown noteworthy progress in the formal methods of analysis, e.g., structural equation models, covariance structure models, multilevel models, and contingency tables. By means of these different methodologies, social scientists try to infer causal relations between variables of interest with reasonable confidence. Data comes from a variety of different sources: surveys, census, experiments, interviews, etc. Analogously, evidence of causal relations can come from different sources: previous studies, background knowledge, knowledge of mechanisms or of probabilistic relations, etc. This pluralistic methodology and source of information raises the question of whether we should accordingly have a pluralistic metaphysics and epistemology. This paper focuses on epistemology and argues that a pluralistic methodology and evidence do not entail a pluralistic epistemology. It will be shown that causal models employ a single rationale of testing, based on the notion of variation. This view of causality profoundly breaks down with the received view, an heritage of Hume, that sees in regularity and/or invariance the key notions for causality. For instance, the rationale of variation clearly emerges in the interpretation of structural equation models: given a system of equations, parameters quantify the variation of the dependent variable due to the variation in the independent variable(s). Regularity and invariance thus become constraints to impose on the variation in order ensure that the model correctly specify the data generating process and that it does not confuse accidental and spurious relations with causal ones. Further, I argue that this monistic epistemology is also involved in alternative philosophical theories of causation, for instance, in probabilistic theories of causality, mechanist and counterfactual accounts, agency-manipulability theories and in the epistemic theory. The philosophical gain in adopting the rationale of variation is at least threefold. First, causality is not merely lodged in a psychological habit of observing regular successions of events. Agreed, we do experience such regular sequences but it is not because of regularity that we interpret them causally. Instead, this is because certain variational relations hold. Second, causality is not reduced to statistics either. Further, to claim that variation is a precondition for regularity and invariance has the advantage of not confusing the rationale of causality with the conditions that allow to interpret variations causally. Third, the adoption of the rationale of variation avoids confusing (i) what causality is (metaphysics) with the notion employed in testing (epistemology) and (ii) with the conditions – e.g. invariance – to impose on the variation to interpret it causally (methodology).
方法论和证据多元论中变异的基本原理
社会科学中的因果分析利用了多种方法和多重信息和证据来源。社会科学中定量因果分析的第一次发展要归功于人口学中的奎特莱(1869)和社会学中的迪尔凯姆(1895和1897)。重要的改进是由于Blalock(1964)和Duncan(1975)。此后,因果分析在结构方程模型、协方差结构模型、多层模型、列联表等形式化分析方法上取得了显著进展。通过这些不同的方法,社会科学家试图以合理的信心推断感兴趣的变量之间的因果关系。数据来自各种不同的来源:调查、人口普查、实验、访谈等。类似地,因果关系的证据可以来自不同的来源:以前的研究、背景知识、机制或概率关系的知识等。这种多元化的方法论和信息来源提出了一个问题,即我们是否应该相应地拥有多元化的形而上学和认识论。本文着重于认识论,并认为多元的方法论和证据并不意味着多元的认识论。它将表明,因果模型采用单一的基本原理的测试,基于变异的概念。这种因果关系的观点与休谟的传统观点截然不同,休谟认为规律和/或不变性是因果关系的关键概念。例如,变异的基本原理清晰地出现在结构方程模型的解释中:给定一个方程系统,参数量化因变量由于自变量的变异而产生的变异。因此,规律性和不变性成为对变化施加的约束,以确保模型正确地指定数据生成过程,并且不会将偶然和虚假的关系与因果关系混淆。此外,我认为这种一元论的认识论也涉及到因果关系的其他哲学理论,例如,在因果关系的概率理论,机械论和反事实的叙述,能动性理论和认识论中。采用变异理论的哲学收益至少有三方面。第一,因果关系不只是存在于观察事件有规律连续的心理习惯中。同意,我们确实经历过这种规律的序列,但这并不是因为规律,我们解释它们的因果关系。相反,这是因为某些变分关系成立。其次,因果关系也不能简化为统计数据。此外,声称变异是规律性和不变性的先决条件,其优点是不会混淆因果关系的基本原理与允许因果关系解释变异的条件。第三,采用变异的基本原理避免了混淆(1)因果关系是什么(形而上学)与用于检验的概念(认识论)和(2)与强加于变异上的条件(例如不变性)来解释它的因果关系(方法论)。
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来源期刊
Argumenta Philosophica
Argumenta Philosophica Arts and Humanities-Visual Arts and Performing Arts
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