By Donald B. Rubin
Matched sampling is usually used to assist determine the causal impression of a few publicity or intervention, often whilst randomized experiments should not on hand or can't be performed. This ebook offers a range of Donald B. Rubin's examine articles on matched sampling, from the early Seventies, while the writer was once one of many significant researchers fascinated by developing the sphere, to fresh contributions to this now super lively quarter. The articles comprise basic theoretical reviews that experience turn into classics, vital extensions, and genuine functions that diversity from breast melanoma remedies to tobacco litigation to experiences of felony traits. they're prepared into seven components, every one with an advent by means of the writer that gives ancient and private context and discusses the relevance of the paintings at the present time. A concluding essay bargains recommendation to investigators designing observational reviews. The ebook offers an obtainable creation to the learn of matched sampling and should be an necessary reference for college students and researchers.
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Additional info for Matched Sampling for Causal Effects
Sample text
Cochran’s thoughts on observational studies were systematically expressed in his seminar on observational studies, Statistics 284, at Harvard University, which 10:3 P1: KAE 0521857627c01 CUFX026-Rubin 0 521 85762 7 June 27, 2006 Cochran’s Contributions to Observational Studies 27 some lucky students attended over the years. Looking through my class notes from 1968, I see many of the points and themes reviewed above. There was, in the seminar relative to his papers, somewhat more review of basic material on the benefits of randomization and, it seems, more emphasis on trying to speculate about the possible sizes of biases.
The adjustment methods were linear regression adjustment for x continuous and direct standardization for x categorical. With x continuous, the range of situations examined included linear relations between y and x, parallel and non-parallel, monotonic non-linear parallel relations, equal and unequal variances of x, and the presence of errors of measurement in x. The percent of initial bias E( y¯1 − y¯2 ) that was removed was used as the criterion. Overall, linear regression adjustment on random samples appeared superior to the matching methods, with linear regression adjustment on matched samples the most robust method.
As an illustration, once, when as a graduate student I was consulting for DATA-TEXT (a Harvard-based statistical computing package), I asked Bill for advice on which of several choices of default options would be best for an analysis of variance routine. He wanted to know what sort of data set was involved, pigs or children or razor blades. I replied that we didn’t know that. He then responded that he realized DATA-TEXT needed an answer, but that since a good answer depended on the properties of the actual data set, DATA-TEXT was asking for a choice among bad answers, and suggested that I ask someone else to provide it.