The Assumptions on Which Causal Inferences Rest
因果推断所依赖的假设AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要明确讨论因果推断假设，并区分均值效应与分布效应的检验。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
关注这篇论文的作者
因果推断所依赖的假设
- 研究问题
- 随机化、无混杂和可忽略处理分配分别支持哪些因果假设检验，为什么不同推断目标需要不同的补充假设？
- 方法与思路
- 作者在一般框架下比较因果假设与识别条件的关系，并通过敏感性分析讨论对假设成立程度的信念。
- 材料陈述的贡献
- 摘要称，随机化或无混杂不足以检验其定义的无因果作用假设，但可支持较弱的无均值效应等假设检验。
- 阅读关注点
- 应核对无因果作用、无均值效应和无分布效应的精确定义，以及潜在响应建模假设如何改变检验能力。
- 阅读边界
- 摘要未给出完整形式化定义和证明，不能将其关于随机化不足的陈述推广为随机试验无法识别任何因果效应。
原文摘要
SUMMARY Statisticians commonly make causal inferences from the results of randomized experiments, but usually question causal inferences from observational studies on the grounds that untestable assumptions are required. This paper explains the basis for this situation and examines, within quite a general framework, various assumptions that appear in the literature. It is demonstrated how the role of each assumption is intimately related to the sort of causal inference being considered. Contrary to general belief, it is shown that neither ‘no confounding’ nor ‘randomization’ are sufficient assumptions for testing the fundamental causal hypothesis of ‘no causation’. They become sufficient, however, if supplemented by a ‘modelling’ assumption for how unobserved covariates affect potential responses. Without supplementary assumptions, no confounding and randomization allow testing of weaker but more important hypotheses, such as ‘no mean effect’. This follows from the sufficiency of an assumption of ‘ignorable treatment assignment’ for testing the hypothesis of ‘no distribution effect‘. It is argued that these ideas will have some operational relevance if we can quantify our beliefs about how closely assumptions hold. to illustrate, a novel form of sensitivity analysis is contrasted with an analysis presented by Rosenbaum and Rubin.