Bayesian Nonparametric Methods For Missing Data And Causal Inference

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Bayesian Nonparametrics for Causal Inference and Missing Data

Author : Michael J. Daniels
Publisher : CRC Press
ISBN 13 : 9780367341008
Total Pages : 0 pages
Book Rating : 4.3/5 (41 download)

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Book Synopsis Bayesian Nonparametrics for Causal Inference and Missing Data by : Michael J. Daniels

Download or read book Bayesian Nonparametrics for Causal Inference and Missing Data written by Michael J. Daniels and published by CRC Press. This book was released on 2023-08-23 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian Nonparametric Methods for Missing Data and Causal Inference provides an overview of flexible Bayesian nonparametric (BNP) methods for modeling joint or conditional distributions and functional relationships, and their interplay with causal inference and missing data. This book emphasizes the importance of making untestable assumptions to identify estimands of interest, such as missing at random assumption for missing data and unconfoundedness for causal inference in observational studies. The BNP approach can account for possible violations of assumptions and minimize concerns about model misspecification, unlike parametric methods. The overall strategy is to first specify BNP models for observed data and second to specify additional uncheckable assumptions to identify estimands of interest. The book is divided into three parts. Part I develops the key concepts in causal inference and missing data, and reviews relevant concepts in Bayesian inference. Part II introduces the fundamental BNP tools required to address causal inference and missing data problems. Part III shows how the BNP approach can be applied in a variety of case studies. The datasets in the case studies come from electronic health records data, survey data, cohort studies, and randomized clinical trials. Features: * Thorough discussion of both BNP and its interplay with causal inference and missing data * How to use BNP and g-computation for causal inference and nonignorable missingness * How to derive and calibrate sensitivity parameters to assess sensitivity to deviations from uncheckable causal and/or missingness assumptions * Detailed case studies illustrating the application of BNP methods to causal inference and missing data * R-code and/or packages to implement BNP in causal inference and missing data problems The book is primarily aimed at researchers and graduate students from statistics and biostatistics. It will also serve as a useful practical reference for mathematically-sophisticated epidemiologists and medical researchers.

Bayesian Nonparametrics for Causal Inference and Missing Data

Author : Michael J. Daniels
Publisher : CRC Press
ISBN 13 : 1000927717
Total Pages : 263 pages
Book Rating : 4.0/5 (9 download)

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Book Synopsis Bayesian Nonparametrics for Causal Inference and Missing Data by : Michael J. Daniels

Download or read book Bayesian Nonparametrics for Causal Inference and Missing Data written by Michael J. Daniels and published by CRC Press. This book was released on 2023-08-23 with total page 263 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian Nonparametrics for Causal Inference and Missing Data provides an overview of flexible Bayesian nonparametric (BNP) methods for modeling joint or conditional distributions and functional relationships, and their interplay with causal inference and missing data. This book emphasizes the importance of making untestable assumptions to identify estimands of interest, such as missing at random assumption for missing data and unconfoundedness for causal inference in observational studies. Unlike parametric methods, the BNP approach can account for possible violations of assumptions and minimize concerns about model misspecification. The overall strategy is to first specify BNP models for observed data and then to specify additional uncheckable assumptions to identify estimands of interest. The book is divided into three parts. Part I develops the key concepts in causal inference and missing data and reviews relevant concepts in Bayesian inference. Part II introduces the fundamental BNP tools required to address causal inference and missing data problems. Part III shows how the BNP approach can be applied in a variety of case studies. The datasets in the case studies come from electronic health records data, survey data, cohort studies, and randomized clinical trials. Features • Thorough discussion of both BNP and its interplay with causal inference and missing data • How to use BNP and g-computation for causal inference and non-ignorable missingness • How to derive and calibrate sensitivity parameters to assess sensitivity to deviations from uncheckable causal and/or missingness assumptions • Detailed case studies illustrating the application of BNP methods to causal inference and missing data • R code and/or packages to implement BNP in causal inference and missing data problems The book is primarily aimed at researchers and graduate students from statistics and biostatistics. It will also serve as a useful practical reference for mathematically sophisticated epidemiologists and medical researchers.

Bayesian Nonparametrics

Author : Nils Lid Hjort
Publisher : Cambridge University Press
ISBN 13 : 1139484605
Total Pages : 309 pages
Book Rating : 4.1/5 (394 download)

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Book Synopsis Bayesian Nonparametrics by : Nils Lid Hjort

Download or read book Bayesian Nonparametrics written by Nils Lid Hjort and published by Cambridge University Press. This book was released on 2010-04-12 with total page 309 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian nonparametrics works - theoretically, computationally. The theory provides highly flexible models whose complexity grows appropriately with the amount of data. Computational issues, though challenging, are no longer intractable. All that is needed is an entry point: this intelligent book is the perfect guide to what can seem a forbidding landscape. Tutorial chapters by Ghosal, Lijoi and Prünster, Teh and Jordan, and Dunson advance from theory, to basic models and hierarchical modeling, to applications and implementation, particularly in computer science and biostatistics. These are complemented by companion chapters by the editors and Griffin and Quintana, providing additional models, examining computational issues, identifying future growth areas, and giving links to related topics. This coherent text gives ready access both to underlying principles and to state-of-the-art practice. Specific examples are drawn from information retrieval, NLP, machine vision, computational biology, biostatistics, and bioinformatics.

Bayesian Data Analysis, Third Edition

Author : Andrew Gelman
Publisher : CRC Press
ISBN 13 : 1439840954
Total Pages : 677 pages
Book Rating : 4.4/5 (398 download)

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Book Synopsis Bayesian Data Analysis, Third Edition by : Andrew Gelman

Download or read book Bayesian Data Analysis, Third Edition written by Andrew Gelman and published by CRC Press. This book was released on 2013-11-01 with total page 677 pages. Available in PDF, EPUB and Kindle. Book excerpt: Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.

An Introduction to Causal Inference

Author : Judea Pearl
Publisher : Createspace Independent Publishing Platform
ISBN 13 : 9781507894293
Total Pages : 0 pages
Book Rating : 4.8/5 (942 download)

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Book Synopsis An Introduction to Causal Inference by : Judea Pearl

Download or read book An Introduction to Causal Inference written by Judea Pearl and published by Createspace Independent Publishing Platform. This book was released on 2015 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper summarizes recent advances in causal inference and underscores the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions that underly all causal inferences, the languages used in formulating those assumptions, the conditional nature of all causal and counterfactual claims, and the methods that have been developed for the assessment of such claims. These advances are illustrated using a general theory of causation based on the Structural Causal Model (SCM) described in Pearl (2000a), which subsumes and unifies other approaches to causation, and provides a coherent mathematical foundation for the analysis of causes and counterfactuals. In particular, the paper surveys the development of mathematical tools for inferring (from a combination of data and assumptions) answers to three types of causal queries: (1) queries about the effects of potential interventions, (also called "causal effects" or "policy evaluation") (2) queries about probabilities of counterfactuals, (including assessment of "regret," "attribution" or "causes of effects") and (3) queries about direct and indirect effects (also known as "mediation"). Finally, the paper defines the formal and conceptual relationships between the structural and potential-outcome frameworks and presents tools for a symbiotic analysis that uses the strong features of both. The tools are demonstrated in the analyses of mediation, causes of effects, and probabilities of causation. -- p. 1.

Nonparametric Bayesian Inference in Biostatistics

Author : Riten Mitra
Publisher : Springer
ISBN 13 : 3319195182
Total Pages : 448 pages
Book Rating : 4.3/5 (191 download)

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Book Synopsis Nonparametric Bayesian Inference in Biostatistics by : Riten Mitra

Download or read book Nonparametric Bayesian Inference in Biostatistics written by Riten Mitra and published by Springer. This book was released on 2015-07-25 with total page 448 pages. Available in PDF, EPUB and Kindle. Book excerpt: As chapters in this book demonstrate, BNP has important uses in clinical sciences and inference for issues like unknown partitions in genomics. Nonparametric Bayesian approaches (BNP) play an ever expanding role in biostatistical inference from use in proteomics to clinical trials. Many research problems involve an abundance of data and require flexible and complex probability models beyond the traditional parametric approaches. As this book's expert contributors show, BNP approaches can be the answer. Survival Analysis, in particular survival regression, has traditionally used BNP, but BNP's potential is now very broad. This applies to important tasks like arrangement of patients into clinically meaningful subpopulations and segmenting the genome into functionally distinct regions. This book is designed to both review and introduce application areas for BNP. While existing books provide theoretical foundations, this book connects theory to practice through engaging examples and research questions. Chapters cover: clinical trials, spatial inference, proteomics, genomics, clustering, survival analysis and ROC curve.

Fundamentals of Nonparametric Bayesian Inference

Author : Subhashis Ghosal
Publisher : Cambridge University Press
ISBN 13 : 0521878268
Total Pages : 671 pages
Book Rating : 4.5/5 (218 download)

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Book Synopsis Fundamentals of Nonparametric Bayesian Inference by : Subhashis Ghosal

Download or read book Fundamentals of Nonparametric Bayesian Inference written by Subhashis Ghosal and published by Cambridge University Press. This book was released on 2017-06-26 with total page 671 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian nonparametrics comes of age with this landmark text synthesizing theory, methodology and computation.

Functional Data Analysis with R

Author : Ciprian M. Crainiceanu
Publisher : CRC Press
ISBN 13 : 1003856306
Total Pages : 338 pages
Book Rating : 4.0/5 (38 download)

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Book Synopsis Functional Data Analysis with R by : Ciprian M. Crainiceanu

Download or read book Functional Data Analysis with R written by Ciprian M. Crainiceanu and published by CRC Press. This book was released on 2024-03-11 with total page 338 pages. Available in PDF, EPUB and Kindle. Book excerpt: Emerging technologies generate data sets of increased size and complexity that require new or updated statistical inferential methods and scalable, reproducible software. These data sets often involve measurements of a continuous underlying process, and benefit from a functional data perspective. Functional Data Analysis with R presents many ideas for handling functional data including dimension reduction techniques, smoothing, functional regression, structured decompositions of curves, and clustering. The idea is for the reader to be able to immediately reproduce the results in the book, implement these methods, and potentially design new methods and software that may be inspired by these approaches. Features: Functional regression models receive a modern treatment that allows extensions to many practical scenarios and development of state-of-the-art software. The connection between functional regression, penalized smoothing, and mixed effects models is used as the cornerstone for inference. Multilevel, longitudinal, and structured functional data are discussed with emphasis on emerging functional data structures. Methods for clustering functional data before and after smoothing are discussed. Multiple new functional data sets with dense and sparse sampling designs from various application areas are presented, including the NHANES linked accelerometry and mortality data, COVID-19 mortality data, CD4 counts data, and the CONTENT child growth study. Step-by-step software implementations are included, along with a supplementary website (www.FunctionalDataAnalysis.com) featuring software, data, and tutorials. More than 100 plots for visualization of functional data are presented. Functional Data Analysis with R is primarily aimed at undergraduate, master's, and PhD students, as well as data scientists and researchers working on functional data analysis. The book can be read at different levels and combines state-of-the-art software, methods, and inference. It can be used for self-learning, teaching, and research, and will particularly appeal to anyone who is interested in practical methods for hands-on, problem-forward functional data analysis. The reader should have some basic coding experience, but expertise in R is not required.

Mixture Models

Author : Weixin Yao
Publisher : CRC Press
ISBN 13 : 1040009875
Total Pages : 398 pages
Book Rating : 4.0/5 (4 download)

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Book Synopsis Mixture Models by : Weixin Yao

Download or read book Mixture Models written by Weixin Yao and published by CRC Press. This book was released on 2024-04-18 with total page 398 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mixture models are a powerful tool for analyzing complex and heterogeneous datasets across many scientific fields, from finance to genomics. Mixture Models: Parametric, Semiparametric, and New Directions provides an up-to-date introduction to these models, their recent developments, and their implementation using R. It fills a gap in the literature by covering not only the basics of finite mixture models, but also recent developments such as semiparametric extensions, robust modeling, label switching, and high-dimensional modeling. Features Comprehensive overview of the methods and applications of mixture models Key topics include hypothesis testing, model selection, estimation methods, and Bayesian approaches Recent developments, such as semiparametric extensions, robust modeling, label switching, and high-dimensional modeling Examples and case studies from such fields as astronomy, biology, genomics, economics, finance, medicine, engineering, and sociology Integrated R code for many of the models, with code and data available in the R Package MixSemiRob Mixture Models: Parametric, Semiparametric, and New Directions is a valuable resource for researchers and postgraduate students from statistics, biostatistics, and other fields. It could be used as a textbook for a course on model-based clustering methods, and as a supplementary text for courses on data mining, semiparametric modeling, and high-dimensional data analysis.

Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives

Author : Andrew Gelman
Publisher : John Wiley & Sons
ISBN 13 : 9780470090435
Total Pages : 448 pages
Book Rating : 4.0/5 (94 download)

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Book Synopsis Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives by : Andrew Gelman

Download or read book Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives written by Andrew Gelman and published by John Wiley & Sons. This book was released on 2004-09-03 with total page 448 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book brings together a collection of articles on statistical methods relating to missing data analysis, including multiple imputation, propensity scores, instrumental variables, and Bayesian inference. Covering new research topics and real-world examples which do not feature in many standard texts. The book is dedicated to Professor Don Rubin (Harvard). Don Rubin has made fundamental contributions to the study of missing data. Key features of the book include: Comprehensive coverage of an imporant area for both research and applications. Adopts a pragmatic approach to describing a wide range of intermediate and advanced statistical techniques. Covers key topics such as multiple imputation, propensity scores, instrumental variables and Bayesian inference. Includes a number of applications from the social and health sciences. Edited and authored by highly respected researchers in the area.

Bayesian Methods in Pharmaceutical Research

Author : Emmanuel Lesaffre
Publisher : CRC Press
ISBN 13 : 1351718665
Total Pages : 583 pages
Book Rating : 4.3/5 (517 download)

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Book Synopsis Bayesian Methods in Pharmaceutical Research by : Emmanuel Lesaffre

Download or read book Bayesian Methods in Pharmaceutical Research written by Emmanuel Lesaffre and published by CRC Press. This book was released on 2020-04-15 with total page 583 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since the early 2000s, there has been increasing interest within the pharmaceutical industry in the application of Bayesian methods at various stages of the research, development, manufacturing, and health economic evaluation of new health care interventions. In 2010, the first Applied Bayesian Biostatistics conference was held, with the primary objective to stimulate the practical implementation of Bayesian statistics, and to promote the added-value for accelerating the discovery and the delivery of new cures to patients. This book is a synthesis of the conferences and debates, providing an overview of Bayesian methods applied to nearly all stages of research and development, from early discovery to portfolio management. It highlights the value associated with sharing a vision with the regulatory authorities, academia, and pharmaceutical industry, with a view to setting up a common strategy for the appropriate use of Bayesian statistics for the benefit of patients. The book covers: Theory, methods, applications, and computing Bayesian biostatistics for clinical innovative designs Adding value with Real World Evidence Opportunities for rare, orphan diseases, and pediatric development Applied Bayesian biostatistics in manufacturing Decision making and Portfolio management Regulatory perspective and public health policies Statisticians and data scientists involved in the research, development, and approval of new cures will be inspired by the possible applications of Bayesian methods covered in the book. The methods, applications, and computational guidance will enable the reader to apply Bayesian methods in their own pharmaceutical research.

Bayesian Data Analysis, Second Edition

Author : Andrew Gelman
Publisher : CRC Press
ISBN 13 : 1420057294
Total Pages : 717 pages
Book Rating : 4.4/5 (2 download)

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Book Synopsis Bayesian Data Analysis, Second Edition by : Andrew Gelman

Download or read book Bayesian Data Analysis, Second Edition written by Andrew Gelman and published by CRC Press. This book was released on 2003-07-29 with total page 717 pages. Available in PDF, EPUB and Kindle. Book excerpt: Incorporating new and updated information, this second edition of THE bestselling text in Bayesian data analysis continues to emphasize practice over theory, describing how to conceptualize, perform, and critique statistical analyses from a Bayesian perspective. Its world-class authors provide guidance on all aspects of Bayesian data analysis and include examples of real statistical analyses, based on their own research, that demonstrate how to solve complicated problems. Changes in the new edition include: Stronger focus on MCMC Revision of the computational advice in Part III New chapters on nonlinear models and decision analysis Several additional applied examples from the authors' recent research Additional chapters on current models for Bayesian data analysis such as nonlinear models, generalized linear mixed models, and more Reorganization of chapters 6 and 7 on model checking and data collection Bayesian computation is currently at a stage where there are many reasonable ways to compute any given posterior distribution. However, the best approach is not always clear ahead of time. Reflecting this, the new edition offers a more pluralistic presentation, giving advice on performing computations from many perspectives while making clear the importance of being aware that there are different ways to implement any given iterative simulation computation. The new approach, additional examples, and updated information make Bayesian Data Analysis an excellent introductory text and a reference that working scientists will use throughout their professional life.

Bayesian Nonparametrics

Author : J.K. Ghosh
Publisher : Springer Science & Business Media
ISBN 13 : 0387226540
Total Pages : 311 pages
Book Rating : 4.3/5 (872 download)

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Book Synopsis Bayesian Nonparametrics by : J.K. Ghosh

Download or read book Bayesian Nonparametrics written by J.K. Ghosh and published by Springer Science & Business Media. This book was released on 2006-05-11 with total page 311 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is the first systematic treatment of Bayesian nonparametric methods and the theory behind them. It will also appeal to statisticians in general. The book is primarily aimed at graduate students and can be used as the text for a graduate course in Bayesian non-parametrics.

Missing Data in Longitudinal Studies

Author : Michael J. Daniels
Publisher : CRC Press
ISBN 13 : 1420011189
Total Pages : 324 pages
Book Rating : 4.4/5 (2 download)

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Book Synopsis Missing Data in Longitudinal Studies by : Michael J. Daniels

Download or read book Missing Data in Longitudinal Studies written by Michael J. Daniels and published by CRC Press. This book was released on 2008-03-11 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: Drawing from the authors' own work and from the most recent developments in the field, Missing Data in Longitudinal Studies: Strategies for Bayesian Modeling and Sensitivity Analysis describes a comprehensive Bayesian approach for drawing inference from incomplete data in longitudinal studies. To illustrate these methods, the authors employ

Oncology Clinical Trials

Author : William Kevin Kelly, DO
Publisher : Springer Publishing Company
ISBN 13 : 0826168736
Total Pages : 568 pages
Book Rating : 4.8/5 (261 download)

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Book Synopsis Oncology Clinical Trials by : William Kevin Kelly, DO

Download or read book Oncology Clinical Trials written by William Kevin Kelly, DO and published by Springer Publishing Company. This book was released on 2018-03-28 with total page 568 pages. Available in PDF, EPUB and Kindle. Book excerpt: The second edition of Oncology Clinical Trials has been thoroughly revised and updated and now contains the latest designs and methods of conducting and analyzing cancer clinical trials in the era of precision medicine with biologic agents—including trials investigating the safety and efficacy of targeted therapies, immunotherapies, and combination therapies as well as novel radiation therapy modalities. Now divided into six sections this revamped book provides the necessary background and expert guidance from the principles governing oncology clinical trials to the innovative statistical design methods permeating the field; from conducting trials in a safe and effective manner, analyzing and interpreting the data, to a forward-looking assessment and discussion of regulatory issues impacting domestic, international, and global clinical trials. Considered by many as the gold standard reference on oncology clinical trials in the field, the second edition continues to provide examples of real-life flaws and real-world examples for how to successfully design, conduct and analyze quality clinical trials and interpret them. With chapters written by oncologists, researchers, biostatisticians, clinical research administrators, and industry and FDA representatives, this volume provides a comprehensive guide in the design, conduct, monitoring, analysis, and reporting of clinical trials in oncology. NEW TO THIS EDITION: Outlines how to design clinical trials with and without biomarker testing—including genomics-based “basket” trials, and adaptive trials for all phases during treatment and quality-of-life trials Includes new chapters on immunotherapy trials, radiation therapy trials, multi-arm trials, meta-analysis and adaptive design, use of genomics, dose modifications and use of ancillary treatments in investigational studies, establishing surrogate endpoints, practical issues with correlative studies, cost-effectiveness analysis, and more Comprehensively covers all regulatory aspects in the pursuit of global oncology trials Digital access to the ebook included

Causal Inference in Statistics

Author : Judea Pearl
Publisher : John Wiley & Sons
ISBN 13 : 1119186862
Total Pages : 162 pages
Book Rating : 4.1/5 (191 download)

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Book Synopsis Causal Inference in Statistics by : Judea Pearl

Download or read book Causal Inference in Statistics written by Judea Pearl and published by John Wiley & Sons. This book was released on 2016-01-25 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt: CAUSAL INFERENCE IN STATISTICS A Primer Causality is central to the understanding and use of data. Without an understanding of cause–effect relationships, we cannot use data to answer questions as basic as "Does this treatment harm or help patients?" But though hundreds of introductory texts are available on statistical methods of data analysis, until now, no beginner-level book has been written about the exploding arsenal of methods that can tease causal information from data. Causal Inference in Statistics fills that gap. Using simple examples and plain language, the book lays out how to define causal parameters; the assumptions necessary to estimate causal parameters in a variety of situations; how to express those assumptions mathematically; whether those assumptions have testable implications; how to predict the effects of interventions; and how to reason counterfactually. These are the foundational tools that any student of statistics needs to acquire in order to use statistical methods to answer causal questions of interest. This book is accessible to anyone with an interest in interpreting data, from undergraduates, professors, researchers, or to the interested layperson. Examples are drawn from a wide variety of fields, including medicine, public policy, and law; a brief introduction to probability and statistics is provided for the uninitiated; and each chapter comes with study questions to reinforce the readers understanding.

Statistical Inference as Severe Testing

Author : Deborah G. Mayo
Publisher : Cambridge University Press
ISBN 13 : 1108563309
Total Pages : 503 pages
Book Rating : 4.1/5 (85 download)

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Book Synopsis Statistical Inference as Severe Testing by : Deborah G. Mayo

Download or read book Statistical Inference as Severe Testing written by Deborah G. Mayo and published by Cambridge University Press. This book was released on 2018-09-20 with total page 503 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mounting failures of replication in social and biological sciences give a new urgency to critically appraising proposed reforms. This book pulls back the cover on disagreements between experts charged with restoring integrity to science. It denies two pervasive views of the role of probability in inference: to assign degrees of belief, and to control error rates in a long run. If statistical consumers are unaware of assumptions behind rival evidence reforms, they can't scrutinize the consequences that affect them (in personalized medicine, psychology, etc.). The book sets sail with a simple tool: if little has been done to rule out flaws in inferring a claim, then it has not passed a severe test. Many methods advocated by data experts do not stand up to severe scrutiny and are in tension with successful strategies for blocking or accounting for cherry picking and selective reporting. Through a series of excursions and exhibits, the philosophy and history of inductive inference come alive. Philosophical tools are put to work to solve problems about science and pseudoscience, induction and falsification.