Download Bias and Causation: Models and Judgment for Valid by Herbert I. Weisberg PDF

By Herbert I. Weisberg

A distinct source on determining and working with bias in statistical study on causal effectsDo cellphones reason melanoma? Can a brand new curriculum elevate scholar fulfillment? settling on what the true motives of such difficulties are, and the way strong their results might be, are crucial 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 via varied assets of bias, even in much less rigorous observational experiences. Bias and Causation offers a whole remedy of the topic, organizing and clarifying the various varieties of biases right into a conceptual framework. The publication treats numerous resources of bias in comparative studies—both randomized and observational—and bargains information on how they need to be addressed via researchers.Utilizing a comparatively uncomplicated mathematical process, the writer develops a idea of bias that outlines the basic nature of the matter and identifies a number of the assets of bias which are encountered in glossy learn. The ebook starts off with an creation to the examine of causal inference and the comparable suggestions and terminology. subsequent, an summary is supplied of the methodological concerns on the middle of the problems posed via bias. next chapters clarify the options of choice bias, confounding, intermediate causal elements, and data bias in addition to the distortion of a causal impression which may consequence whilst the publicity and/or the end result is measured with errors. The publication concludes with a brand new class of twenty normal assets of bias and functional recommendation on how mathematical modeling and specialist judgment might be mixed to accomplish the main credible causal conclusions.Throughout the e-book, examples from the fields of medication, public coverage, and schooling are integrated into the presentation of assorted subject matters. furthermore, six specified case reports illustrate concrete examples of the importance of biases in daily research.Requiring just a simple figuring out of facts and likelihood idea, Bias and Causation is a wonderful complement for classes on examine equipment and utilized statistics on the upper-undergraduate and graduate point. it's also a beneficial reference for training researchers and methodologists in quite a few fields of research who paintings with statistical data.This publication is the winner of the 2010 PROSE Award for arithmetic from the yank Publishers Awards for pro and Scholarly Excellence

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Extra info for Bias and Causation: Models and Judgment for Valid Comparisons (Wiley Series in Probability and Statistics)

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If a component cause appears in all possible sufficient causes, then it can be considered a necessary cause for the event to occur. In the illustration, component 3 would be necessary, because it is a part of all three sufficient causes. 2 causality and counterfactuals 29 As a (grossly oversimplified) concrete example, suppose that five factors determine whether a particular individual dies from an influenza epidemic: Factor Factor Factor Factor Factor 1: 2: 3: 4: 5: Not inoculated with seasonal flu vaccine Age over 75 Exposed to the virus causing the epidemic No access to medical care Weakened immune system Suppose that Fig.

Understanding when, how, and for whom antidepressants should be prescribed will require years to unravel. As in the HRT situation, there is much grist here for the methodological mills to grind. GUIDEPOST 1 This chapter has introduced the topic of bias in comparative studies and presented several case studies that illustrate both the importance and the difficulties inherent in causal inference. These examples were presented against a backdrop of introductory ideas that emphasized the limitations of classical statistical theory for causal inference.

3 Now suppose we ask the following counterfactual question: what would have happened with Factor 1 absent, but everything else in this scenario remaining fixed? 4. An individual for whom the outcome event occurs (indicated by shading) whether or not Factor 1 is present. Therefore, Factor 1 has no causal effect for this individual. 5. An individual for whom the outcome event occurs if and only if Factor 1 occurs. Therefore, Factor 1 is a cause of the event for this individual. other factors besides Factor 1.

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