A First Course in Causal Inference by Peng Ding Hardcover Book

Description: A First Course in Causal Inference by Peng Ding This textbook, based on the authors course on causal inference at UC Berkeley taught over the past seven years, only requires basic knowledge of probability theory, statistical inference, and linear and logistic regressions. It assumes minimal knowledge of causal inference. FORMAT Hardcover CONDITION Brand New Publisher Description The past decade has witnessed an explosion of interest in research and education in causal inference, due to its wide applications in biomedical research, social sciences, artificial intelligence etc. This textbook, based on the authors course on causal inference at UC Berkeley taught over the past seven years, only requires basic knowledge of probability theory, statistical inference, and linear and logistic regressions. It assumes minimal knowledge of causal inference, and reviews basic probability and statistics in the appendix. It covers causal inference from a statistical perspective and includes examples and applications from biostatistics and econometrics.Key Features:All R code and data sets available at Harvard Dataverse.Solutions manual available for instructors.Includes over 100 exercises.This book is suitable for an advanced undergraduate or graduate-level course on causal inference, or postgraduate and PhD-level course in statistics and biostatistics departments. Author Biography Peng Ding is an Associate Professor in the Department of Statistics at UC Berkeley. His research focuses on causal inference and its applications. Table of Contents PrefacePart 1: Introduction 1. Correlation, Association, and the Yule–Simpson Paradox2. Potential OutcomesPart 2: Randomized experiments 3. The Completely Randomized Experiment and the Fisher Randomization Test4. Neymanian Repeated Sampling Inference in Completely Randomized Experiments5. Stratification and Post-Stratification in Randomized Experiments6. Rerandomization and Regression Adjustment7. Matched-Pairs Experiment8. Unification of the Fisherian and Neymanian Inferences in Randomized Experiments9. Bridging Finite and Super Population Causal InferencePart 3: Observational studies 10. Observational Studies, Selection Bias, and Nonparametric Identification of Causal Effects11. The Central Role of the Propensity Score in Observational Studies for Causal Effects12. The Doubly Robust or the Augmented Inverse Propensity Score Weighting Estimator for the Average Causal Effect13. The Average Causal Effect on the Treated Units and Other Estimands14. Using the Propensity Score in Regressions for Causal Effects15. Matching in Observational StudiesPart 4: Difficulties and challenges of observational studies 16. Difficulties of Unconfoundedness in Observational Studies for Causal Effects17. E-Value: Evidence for Causation in Observational Studies with Unmeasured Confounding18. Sensitivity Analysis for the Average Causal Effect with Unmeasured Confounding19. Rosenbaum-Style p-Values for Matched Observational Studies with Unmeasured Confounding20. Overlap in Observational Studies: Difficulties and OpportunitiesPart 5: Instrumental variables 21. An Experimental Perspective of the Instrumental Variable22. Disentangle Mixture Distributions and Instrumental Variable Inequalities23. An Econometric Perspective of the Instrumental Variable24. Application of the Instrumental Variable Method: Fuzzy Regression Discontinuity25. Application of the Instrumental Variable Method: Mendelian RandomizationPart 6: Causal Mechanisms with Post-Treatment Variables 26. Principal Stratification27. Mediation Analysis: Natural Direct and Indirect Effects28. Controlled Direct Effect29. Time-Varying Treatment and ConfoundingPart 7: Appendices A. Probability and StatisticsB. Linear and Logistic RegressionsC. Some Useful Lemmas for Simple Random Sampling From a Finite Population Review "This book offers a statisticians perspective on causal inference. It provides an invaluable review of statistical paradoxes in causal inference from observational data, linking those paradoxes to Pearls directed acyclic graphs (DAGs). The overview of the literature on matching is the best that Ive seen, and the inclusion of R code is a huge plus. The book would make a great introduction (and more) to advanced undergraduate and masters programs in statistics."Professor Bryan Dowd, University of Minneapolis, U.S.A."A First Course in Causal Inference by Peng Ding is written by an authority in the field at technical level that makes it stand out from existing textbooks on causal inference. It will be a welcome resource for students and researchers in public health, medicine, and the social sciences who have a good background in math and statistics. Exercises lead readers through important results, appendices review key mathematical and statistical concepts, and the book contains well-written R code that will be extremely useful for translating theory into practice."Professor Eben Kenah, The Ohio State University, U.S.A."Professor Ding accomplished something impressive with this book — a clear, precise, and thorough introduction to Causal Inference. This book is a must-have for anyone interested in understanding the subject. I highly recommend it."Professor Hugo Jales, Syracuse University, Maxwell School of Citizenship & Public Affairs, USA. Details ISBN1032758627 Author Peng Ding Publisher Taylor & Francis Ltd Series Chapman & Hall/CRC Texts in Statistical Science Year 2024 ISBN-13 9781032758626 Format Hardcover Imprint Chapman & Hall/CRC Country of Publication United Kingdom Illustrations 11 Tables, black and white; 51 Line drawings, black and white; 51 Illustrations, black and white Audience Tertiary & Higher Education DEWEY 519.54 ISBN-10 1032758627 Pages 422 Publication Date 2024-07-31 UK Release Date 2024-07-31 We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! TheNile_Item_ID:160887928;

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