Credible Causal Inference for Empirical Legal Studies
Credible Causal Inference for Empirical Legal Studies
复制标题
实证法律研究的可信因果推理
DOI：
10.1146/annurev-lawsocsci-102510-105423
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发表时间：
2011
影响因子：
2.4
通讯作者：
D. Rubin
中科院分区：
文献类型：
--
作者：
Daniel E. Ho;D. Rubin
我们回顾了可信的因果推理的进展，有广泛的应用经验法律的研究。我们的主要观点很简单：研究设计胜过分析方法。我们解释匹配和回归不连续的方法在直观（非技术）方面。为了说明这一点，我们将这些数据应用于监狱设施对囚犯不当行为影响的现有数据，并与实验证据进行比较。将现代因果推理方法统一起来的是研究设计的优先级，以在不参考任何结果数据的情况下确定可比单位的子集。在这些亚组中，结果差异可合理地归因于暴露于治疗而非对照条件。传统的分析方法在这场冒险中发挥了很小的作用。法律中可信的因果推理依赖于实质性的法律的知识，而不是数学知识。
We review advances toward credible causal inference that have wide application for empirical legal studies. Our chief point is simple: Research design trumps methods of analysis. We explain matching and regression discontinuity approaches in intuitive (nontechnical) terms. To illustrate, we apply these to existing data on the impact of prison facilities on inmate misconduct, which we compare to experimental evidence. What unifies modern approaches to causal inference is the prioritization of research design to create—without reference to any outcome data—subsets of comparable units. Within those subsets, outcome differences may then be plausibly attributed to exposure to the treatment rather than control condition. Traditional methods of analysis play a small role in this venture. Credible causal inference in law turns on substantive legal, not mathematical, knowledge.
影响因子：
5
作者：
M. Joffe;P. Rosenbaum
通讯作者：
M. Joffe;P. Rosenbaum