Causal Assessment in Small-N Policy Studies
Causal Assessment in Small-N Policy Studies
复制标题
小N政策研究中的因果评估
DOI：
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发表时间：
2007
期刊：
影响因子：
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通讯作者：
P. Steinberg
中科院分区：
文献类型：
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作者：
P. Steinberg
因果关系的识别在政策研究中起着不可或缺的作用，无论是对应用问题的解决还是对政策过程理论的构建。历史过程追踪已经成为一种很有前途的方法，可以通过统计技术以无法达到的精确度揭示因果机制。然而，历史分析往往会产生令人生畏的复杂因果解释，许多因素成为必然但不充分的结果原因。本文介绍了一种方法，使复杂的因果关系叙述更易于分析，通过建立衡量标准，排名的相对重要性的组成部分的原因。通过专注于主观有用的测量属性，该方法非常适合政策科学的独特组合，明确规范的愿望和承诺的因果索赔的系统评估。公共政策目标的核心，以及支持这些目标的政治群体，是希望对世界的某些方面产生因果影响。人们希望，从福利到工作的计划将导致长期失业率的下降;国际捕鲸制度将导致受威胁物种的反弹;健康教育运动将减少艾滋病毒的传播。正如Pressman和Wildavsky（1973，p. xxi）所观察到的，“政策意味着理论。无论是否明确说明，政策都指向初始条件和未来后果之间的因果关系链。如果X，那么Y。”因此，虽然因果理论在社会调查的许多领域发挥作用，但它们对政策分析的实践至关重要，它们用于诊断问题，预测新法规的未来影响，并评估过去干预措施的有效性并分配责任（Chen，1990; Lin，1998; Young，1999）。因果评估在政策过程传统中扮演着同样重要的角色，因为研究人员确定了影响政策议程，决策风格，国家-社会关系以及稳定和变化动态的因果因素（Baumgartner & Jones，1993; Rochon & Mazmanian，1993; Sabatier，1999）。在此背景下，本文重点讨论了一个对政策导向的政治学家特别重要的问题：如何评估小N研究环境中的因果影响。这个问题的动机是一个对政策研究来说再熟悉不过的情景
The identification of cause-and-effect relationships plays an indispensable role in policy research, both for applied problem solving and for building theories of policy processes. Historical process tracing has emerged as a promising method for revealing causal mechanisms at a level of precision unattainable through statistical techniques. Yet historical analyses often produce dauntingly complex causal explanations, with numerous factors emerging as necessary but insufficient causes of an outcome. This article describes an approach that renders complex causal narratives more analytically tractable by establishing measurement criteria for ranking the relative importance of component causes. By focusing on subjectively useful measurement attributes, the approach is well suited to the policy sciences’ unique combination of explicitly normative aspirations and a commitment to the systematic assessment of causal claims. Central to the aims of public policies, and the political constituencies supporting them, is the hope of having a causal impact on some aspect of the world. It is hoped that welfare-to-work programs will lead to a decline in chronic unemployment; that the international whaling regime will cause threatened species to rebound; and that health education campaigns will reduce HIV transmission. As Pressman and Wildavsky (1973, p. xxi) observed, “Policies imply theories. Whether stated explicitly or not, policies point to a chain of causation between initial conditions and future consequences. If X, then Y.” Accordingly, while causal theories play a role in many areas of social inquiry, they are vital to the practice of policy analysis, where they are used to diagnose problems, project future impacts of new regulations, and evaluate the effectiveness of—and assign responsibility for—past interventions (Chen, 1990; Lin, 1998; Young, 1999). Causal assessment plays an equally important role in the policy process tradition, as researchers identify the causal factors shaping policy agendas, decision-making styles, state–society relations, and the dynamics of stability and change (Baumgartner & Jones, 1993; Rochon & Mazmanian, 1993; Sabatier, 1999). Against this backdrop, this article focuses on an issue of special importance to policy-oriented political scientists: how to assess causal impacts in small-N research settings. This question is motivated by a scenario all too familiar to policy research