Applied Bayesian Modeling and Causal Inference from by Andrew Gelman, Xiao-Li Meng

By Andrew Gelman, Xiao-Li Meng

Statistical thoughts that take account of lacking info in a medical trial, census, or different experiments, observational experiences, and surveys are of accelerating significance. using more and more robust desktops and algorithms has made it attainable to review statistical difficulties from a Bayesian point of view. those issues are hugely energetic learn components and feature vital purposes throughout quite a lot of disciplines.
This e-book is a suite of articles from best researchers on statistical equipment with regards to lacking facts research, causal inference, and statistical modeling, together with a number of imputation, propensity rankings, instrumental variables, and Bayesian inference. The booklet is devoted to Professor Donald Rubin, at the social gathering of his sixtieth birthday, in acceptance of his many and wide-ranging contributions to stats, quite to the subject of statistical research with lacking data.

Provides an authoritative evaluation of numerous vital statistical subject matters for either study and applications.
Adopts a practical method of describing a variety of intermediate and complicated statistical techniques.
Covers key subject matters akin to a number of imputation, propensity rankings, instrumental variables and Bayesian inference.
Includes a variety of purposes from the social, future health, organic, and actual sciences.
Features evaluation chapters for every a part of the book.
Edited and authored by way of hugely revered researchers within the area.
Applied Bayesian Modeling and Causal Inference from Incomplete-Data views provides an outline with examples of those key themes compatible for researchers in all parts of data. It adopts a pragmatic technique compatible for utilized statisticians operating in social and political sciences, organic and scientific sciences, and actual sciences, in addition to graduate scholars of statistics and biostatistics.

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Additional resources for Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives (Wiley Series in Probability and Statistics)

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The percentages represent the proportion of the population matching that specific characteristic.

That is, although a certain covariate u is not measured, u is known to be substantially higher in one control group than in the other. If the two control groups have similar outcomes, which are very different from the outcomes in the treated group, then this is consistent with a treatment effect as opposed to bias from u, whereas substantial differences in outcomes between the two control groups cannot be explained as a treatment effect and are consistent with bias from u. An interesting example is found in Card and Krueger’s (1994) study of the effects of the minimum wage on employment in the fast food industry.

In this case, cost considerations may require some form of sampling of the large reservoir of potential controls. In matched sampling, potential controls are drawn from the reservoir to be similar to the treated group in terms of available covariates, so the sampling process both reduces cost and begins to remove bias due to x. Matching increases robustness of model-based adjustments. Matching can be used as a method for sampling controls or alternatively as an analytical method that retains many or all available controls.

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