Identification Problems in the Social Sciences

社会科学中的识别问题

• 批准号: 0001436
• 负责人: Charles Manski
• 金额: $ 19.65万
• 依托单位: Northwestern University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-07-15 至 2006-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0001436&HistoricalAwards=false
• 关键词: Identification; Problems; Social; Sciences

Econometricians have long found it useful to separate inferential problems into statistical and identification components. Studies of identification seek to characterize the conclusions that could be drawn if the researcher had available a sample of unlimited size. Studies of statistical inference seek to characterize the generally weaker conclusions that can be drawn given a sample of positive but finite size. Statistical and identification problems limit in distinct ways the conclusions that may be drawn in empirical research. Statistical problems are most severe when the available sample is small. Identification problems are most severe when the researcher knows little about the population under study and the sampling process yields only weak data on the population.The proposed research will continue the investigator's program of work on identification problems in the social sciences. This program is deliberately conservative in the assumptions that it brings to bear. The investigator believes that it is not sufficient for empirical researchers to know the inferences that can be made if assumptions strong enough to yield identification are imposed. It is also important to characterize the inferences that can be made without imposing such assumptions, which often lack credibility. Conservative analysis allows the establishment of a domain of consensus among researchers who may hold disparate beliefs about what assumptions are appropriate.The investigator will study how empirical analysis of treatment response informs the normative problem of treatment choice in heterogeneous populations. Recent work by the investigator has shown how identification problems generate ambiguity about the identity of optimal treatment rules. The planned research will analyze the complementary statistical problem of induction from finite samples to populations, which generates further ambiguity. The long-run objective is a coherent integrated analysis of identification and statistical inference.The investigator will also study problems of incomplete identification of regressions that arise under empirically relevant sampling processes. One problem to be examined is inference on long regressions using data on short regressions. This research has immediate application to the longstanding ecological inference problem. Another problem to be analyzed is inference on regressions when only interval measures of covariates are available.

长期以来，计量经济学家发现将推理问题分为统计和识别两部分是有用的。认同研究试图描述研究者在拥有无限样本的情况下所能得出的结论。统计推断的研究试图描述一般较弱的结论，可以得出一个积极的，但有限大小的样本。统计和识别问题以不同的方式限制了实证研究中可能得出的结论。当可用样本较少时，统计问题最为严重。当研究人员对所研究的人口知之甚少，抽样过程只产生关于人口的薄弱数据时，识别问题最为严重。这个程序在它所承担的假设中是故意保守的。研究者认为，如果强加了强到足以产生认同的假设，那么实证研究者仅仅知道可以做出的推论是不够的。同样重要的是，要描述在不强加这些假设的情况下可以做出的推论，因为这些假设往往缺乏可信度。保守分析允许在那些对什么样的假设是适当的持有不同信念的研究者之间建立一个共识域，研究者将研究治疗反应的经验分析如何告知异质人群中治疗选择的规范性问题。最近的研究工作表明，如何识别问题产生的最佳治疗规则的身份模糊。计划中的研究将分析从有限样本到总体归纳的补充统计问题，这会产生进一步的模糊性。长期目标是对识别和统计推断进行连贯的综合分析，研究者还将研究在经验相关的抽样过程中出现的回归不完全识别问题。要研究的一个问题是使用短期回归数据推断长期回归。这项研究已立即应用于长期存在的生态推理问题。另一个要分析的问题是当协变量的区间测度可用时对回归的推断。