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Identification for prediction and decision
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ISBN: 9780674026537 0674026535 0674033663 Year: 2007 Publisher: Cambridge: Harvard university press,

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This book is a full-scale exposition of Charles Manski's new methodology for analyzing empirical questions in the social sciences. He recommends that researchers first ask what can be learned from data alone, and then ask what can be learned when data are combined with credible weak assumptions. Inferences predicated on weak assumptions, he argues, can achieve wide consensus, while ones that require strong assumptions almost inevitably are subject to sharp disagreements.Building on the foundation laid in the author's "Identification Problems in the Social Sciences" (Harvard, 1995), the book's fifteen chapters are organized in three parts. Part I studies prediction with missing or otherwise incomplete data. Part II concerns the analysis of treatment response, which aims to predict outcomes when alternative treatment rules are applied to a population. Part III studies prediction of choice behaviour.Each chapter juxtaposes developments of methodology with empirical or numerical illustrations. The book employs a simple notation and mathematical apparatus, using only basic elements of probability theory.


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Analog estimation methods in econometrics
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ISBN: 0412011417 9780412011412 Year: 1988 Publisher: New York: Chapman and Hall,

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Social choice with partial knowledge of treatment response
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ISBN: 0691121532 9780691121536 0691217734 Year: 2005 Publisher: Princeton: Princeton university press,


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Public policy in an uncertain world
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ISBN: 0674067541 0674066898 9780674067547 0674071301 9780674066892 9780674071308 Year: 2013 Publisher: Cambridge, Mass. Harvard University Press

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Public policy advocates routinely assert that "research has shown" a particular policy to be desirable. But how reliable is the analysis in the research they invoke? And how does that analysis affect the way policy is made, on issues ranging from vaccination to minimum wage to FDA drug approval? Charles Manski argues here that current policy is based on untrustworthy analysis. By failing to account for uncertainty in an unpredictable world, policy analysis misleads policy makers with expressions of certitude. Public Policy in an Uncertain World critiques the status quo and offers an innovation to improve how policy research is conducted and how policy makers use research. Consumers of policy analysis, whether civil servants, journalists, or concerned citizens, need to understand research methodology well enough to properly assess reported findings. In the current model, policy researchers base their predictions on strong assumptions. But as Manski demonstrates, strong assumptions lead to less credible predictions than weaker ones. His alternative approach takes account of uncertainty and thereby moves policy analysis away from incredible certitude and toward honest portrayal of partial knowledge. Manski describes analysis of research on such topics as the effect of the death penalty on homicide, of unemployment insurance on job-seeking, and of preschooling on high school graduation. And he uses other real-world scenarios to illustrate the course he recommends, in which policy makers form reasonable decisions based on partial knowledge of outcomes, and journalists evaluate research claims more closely, with a skeptical eye toward expressions of certitude.

Partial identification of probability distributions
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ISBN: 0387004548 9786610188284 1280188286 038721786X 9780387004549 Year: 2003 Publisher: New York: Springer,

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Sample data alone never suffice to draw conclusions about populations. Inference always requires assumptions about the population and sampling process. Statistical theory has revealed much about how strength of assumptions affects the precision of point estimates, but has had much less to say about how it affects the identification of population parameters. Indeed, it has been commonplace to think of identification as a binary event – a parameter is either identified or not – and to view point identification as a pre-condition for inference. Yet there is enormous scope for fruitful inference using data and assumptions that partially identify population parameters. This book explains why and shows how. The book presents in a rigorous and thorough manner the main elements of Charles Manski’s research on partial identification of probability distributions. One focus is prediction with missing outcome or covariate data. Another is decomposition of finite mixtures, with application to the analysis of contaminated sampling and ecological inference. A third major focus is the analysis of treatment response. Whatever the particular subject under study, the presentation follows a common path. The author first specifies the sampling process generating the available data and asks what may be learned about population parameters using the empirical evidence alone. He then ask how the (typically) setvalued identification regions for these parameters shrink if various assumptions are imposed. The approach to inference that runs throughout the book is deliberately conservative and thoroughly nonparametric. Conservative nonparametric analysis enables researchers to learn from the available data without imposing untenable assumptions. It enables establishment of a domain of consensus among researchers who may hold disparate beliefs about what assumptions are appropriate. Charles F. Manski is Board of Trustees Professor at Northwestern University. He is author of Identification Problems in the Social Sciences and Analog Estimation Methods in Econometrics. He is a Fellow of the American Academy of Arts and Sciences, the American Association for the Advancement of Science, and the Econometric Society.


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Policy Analysis with Incredible Certitude
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Year: 2010 Publisher: Cambridge, Mass. National Bureau of Economic Research

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WHEN CONSENSUS CHOICE DOMINATES INDIVIDUALISM: Jensen's Inequality and Collective Decisions under Uncertainty
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Year: 2009 Publisher: Cambridge, Mass. National Bureau of Economic Research

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Partial Prescriptions For Decisions With Partial Knowledge
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Year: 2008 Publisher: Cambridge, Mass. National Bureau of Economic Research

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Adaptive Minimax-Regret Treatment Choice, With Application To Drug Approval
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Year: 2007 Publisher: Cambridge, Mass. National Bureau of Economic Research

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