Identification Problems in the Social Sciences

计量经济学家早就发现，将推理问题分成统计和识别两部分是很有用的。鉴定研究试图确定如果研究人员有一个无限大小的样本可以得出的结论的特征。统计推断的研究试图描述一般较弱的结论，这些结论可以在给定的正但有限的样本中得出。统计和鉴定问题以不同的方式限制了实证研究可能得出的结论。当可用的样本很少时，统计问题是最严重的。当研究人员对所研究的群体知之甚少，抽样过程只产生关于群体的微弱数据时，识别问题是最严重的。拟议的研究将继续研究者在社会科学中识别问题的工作计划。这个程序在它所承担的假设中故意保守。研究者认为，如果假设强到足以产生识别，经验研究人员知道可以做出的推论是不够的。同样重要的是，在不强加这种假设的情况下，对往往缺乏可信度的推断进行特征描述。保守分析允许在研究人员之间建立一个共识领域，这些研究人员可能对哪些假设是适当的持有不同的信念。研究者将研究治疗反应的实证分析如何告知异质性人群中治疗选择的规范问题。研究者最近的工作表明，识别问题如何产生最优治疗规则的模糊性。计划中的研究将分析从有限样本到总体的互补统计问题，这将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采样过程中出现的不完全识别回归的问题。要研究的一个问题是使用短回归的数据对长回归进行推断。该研究对长期存在的生态推理问题具有直接的应用价值。另一个要分析的问题是当只有协变量的区间度量可用时对回归的推断。

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.