Herbert I. Weisberg's Bias and Causation: Models and Judgment for Valid PDF

By Herbert I. Weisberg

ISBN-10: 0470286393

ISBN-13: 9780470286395

A extraordinary source on selecting and working with bias in statistical examine on causal effectsDo cellphones reason melanoma? Can a brand new curriculum raise scholar success? picking out what the true explanations of such difficulties are, and the way robust their results could be, are primary matters in learn throughout quite a few fields of analysis. a few researchers are hugely skeptical of drawing causal conclusions other than in tightly managed randomized experiments, whereas others the threats posed through diversified resources of bias, even in much less rigorous observational experiences. Bias and Causation offers an entire remedy of the topic, organizing and clarifying the various varieties of biases right into a conceptual framework. The booklet treats a variety of assets of bias in comparative studies—both randomized and observational—and deals assistance on how they need to be addressed by means of researchers.Utilizing a comparatively uncomplicated mathematical process, the writer develops a concept of bias that outlines the fundamental nature of the matter and identifies a number of the resources of bias which are encountered in glossy examine. The booklet starts with an creation to the learn of causal inference and the similar techniques and terminology. subsequent, an summary is supplied of the methodological concerns on the middle of the problems posed through bias. next chapters clarify the options of choice bias, confounding, intermediate causal elements, and knowledge bias besides the distortion of a causal impact which could end result whilst the publicity and/or the result is measured with errors. The booklet concludes with a brand new type of twenty basic resources of bias and useful suggestion on how mathematical modeling and specialist judgment might be mixed to accomplish the main credible causal conclusions.Throughout the publication, examples from the fields of medication, public coverage, and schooling are integrated into the presentation of assorted subject matters. moreover, six precise case stories illustrate concrete examples of the importance of biases in daily research.Requiring just a simple realizing of data and likelihood concept, Bias and Causation is a wonderful complement for classes on examine equipment and utilized facts on the upper-undergraduate and graduate point. it's also a helpful reference for training researchers and methodologists in numerous fields of research who paintings with statistical data.This booklet is the winner of the 2010 PROSE Award for arithmetic from the yank Publishers Awards for pro and Scholarly Excellence

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Counterfactual concepts provide a useful “language” in which such questions can be discussed. The counterfactual perspective is not a unique conceptual framework for addressing such questions meaningfully. There are certainly others. Although counterfactual thinking has become extremely popular recently, it is by no means universally accepted by philosophers of science or research methodologists. Energetic debate about the merits of the approach persists (Holland, 1986, with discussion; Dawid, 2000, with discussion; Maldonado and Greenland, 2002, with discussion).

The approach adopted throughout this book is to utilize basic algebra and statistical concepts as devices to help methodologists formulate (and sometimes answer) questions that arise in practice. For example, every researcher is aware that allowing individuals to choose whether to participate in a treatment group or a control group is a recipe for bias. But exactly why and in what sense is this true? Are there circumstances in which such a design will not produce bias? When can the extent of possible bias be estimated?

Weisberg Copyright © 2010 John Wiley & Sons, Inc. 1 bias and causation 25 of causation directly. Rather, they cleverly sidestep this issue by creating special situations in which bias can be avoided. In this way, a causal effect can be estimated without having to worry about what this effect really means. To assess whether an observed effect reflects causation or mere coincidence entails considerations beyond the purview of purely mathematical analysis. External information derived from background knowledge, intuition, or theory may be required to identify relationships that a scientist would normally describe as causal.

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Bias and Causation: Models and Judgment for Valid Comparisons (Wiley Series in Probability and Statistics) by Herbert I. Weisberg


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