Robustness - A New Methodology for Causal Inference
鲁棒性——因果推理的新方法
基本信息
- 批准号:ES/L003163/1
- 负责人:
- 金额:$ 25.18万
- 依托单位:
- 依托单位国家:英国
- 项目类别:Research Grant
- 财政年份:2013
- 资助国家:英国
- 起止时间:2013 至 无数据
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Empirical social scientists are interested in testing hypotheses about the real world derived from theories and to provide an evidence base for policy and decision-making. To do so they need to engage in what is known as causal inference, which consists of identifying a causal effect (rather than a mere correlation between two events), understanding the mechanism driving the causal effect, and drawing conclusions over the general existence of a causal relation based on observation or analysis of a limited number of cases to a wider population.Social scientists have a large number of strategies or techniques for making causal inferences at their disposal: They can undertake a hypothetical counterfactual analysis for a single case or a few cases. They can analyze a selected individual case over time or compare a few selected cases cross-sectionally or over time. They can use regression analysis based on observational data to analyze a large number of cases. Finally, they can study a sample of selected cases in which treatment assignment is randomized, or they can conduct experiments that randomize both sample and treatment.Of these techniques, regression analysis based on observational data is the most popular because of essentially two reasons. First, if based on a randomly drawn sample from the overall set of relevant cases, then regression analysis produces results that can be generalized beyond the sample studied. Second, regression analysis is extremely versatile and can be deployed wherever observational data exist based on censuses, surveys or administrative records. However, regression analysis only results in reliable inferences if one's estimation model is correctly specified.Regression analysis as currently undertaken does not provide reliable inferences, however, because analysts cannot know the extent to which their models are correctly specified. Our proposed new methodology will allow analysts to come to more reliable inferences by enabling them to test whether their inferences uphold in the face of plausible changes to model specification or are dependent on certain model specification choices. Inferences that are robust to plausible changes to model specification can command a much larger confidence, that is, such inferences are much more reliable. In sum, then, the aim of the project is to place regression analysis based on observational data - the workhorse technique of empirical social science - on a sounder methodological foundation.
实证社会科学家感兴趣的是测试来自理论的关于真实的世界的假设,并为政策和决策提供证据基础。要做到这一点,他们需要从事所谓的因果推理,其中包括确定因果效应(而不仅仅是两个事件之间的相关性),理解驱动因果效应的机制,社会科学家有大量的策略和技术,他们可以对一个或几个案例进行假设性的反事实分析。他们可以分析一段时间内选定的个别案件,或比较几个选定的案件横截面或随着时间的推移。他们可以使用基于观察数据的回归分析来分析大量案例。最后,他们可以研究随机分配治疗的选定病例的样本,或者他们可以进行随机样本和治疗的实验。在这些技术中,基于观察数据的回归分析是最流行的,主要有两个原因。首先,如果基于从相关案例的整体集合中随机抽取的样本,则回归分析产生的结果可以推广到所研究的样本之外。第二,回归分析的用途极为广泛,可用于任何基于人口普查、调查或行政记录的观测数据。然而,回归分析只有在正确指定估计模型的情况下才能得出可靠的推论,而目前进行的回归分析并不能提供可靠的推论,因为分析人员无法知道他们的模型被正确指定的程度。我们提出的新方法将允许分析师来更可靠的推论,使他们能够测试他们的推论是否坚持在面对合理的变化,模型规格或依赖于某些模型规格的选择。对模型规格的合理变化具有鲁棒性的推断可以命令更大的置信度,也就是说,这样的推断更加可靠。总之,该项目的目的是将基于观测数据的回归分析-经验社会科学的主力技术-置于更健全的方法论基础之上。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Thomas Pluemper其他文献
Thomas Pluemper的其他文献
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{{ truncateString('Thomas Pluemper', 18)}}的其他基金
Essex Summer School in Social Science Data Analysis and Collection
埃塞克斯社会科学数据分析与收集暑期学校
- 批准号:
PTA-035-25-0036 - 财政年份:2006
- 资助金额:
$ 25.18万 - 项目类别:
Research Grant
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