A First Course in Causal Inference: Chapman & Hall/CRC Texts in Statistical Science
Autor Peng Dingen Limba Engleză Hardback – 31 iul 2024
Key Features:
- All R code and data sets available at Harvard Dataverse.
- Solutions manual available for instructors.
- Includes over 100 exercises.
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Specificații
ISBN-13: 9781032758626
ISBN-10: 1032758627
Pagini: 448
Ilustrații: 102
Dimensiuni: 178 x 254 x 29 mm
Greutate: 0.99 kg
Ediția:1
Editura: CRC Press
Colecția Chapman and Hall/CRC
Seria Chapman & Hall/CRC Texts in Statistical Science
Locul publicării:Boca Raton, United States
ISBN-10: 1032758627
Pagini: 448
Ilustrații: 102
Dimensiuni: 178 x 254 x 29 mm
Greutate: 0.99 kg
Ediția:1
Editura: CRC Press
Colecția Chapman and Hall/CRC
Seria Chapman & Hall/CRC Texts in Statistical Science
Locul publicării:Boca Raton, United States
Public țintă
Postgraduate and Undergraduate AdvancedCuprins
Preface
Part 1: Introduction
1. Correlation, Association, and the Yule–Simpson Paradox
2. Potential Outcomes
Part 2: Randomized experiments
3. The Completely Randomized Experiment and the Fisher Randomization Test
4. Neymanian Repeated Sampling Inference in Completely Randomized Experiments
5. Stratification and Post-Stratification in Randomized Experiments
6. Rerandomization and Regression Adjustment
7. Matched-Pairs Experiment
8. Unification of the Fisherian and Neymanian Inferences in Randomized Experiments
9. Bridging Finite and Super Population Causal Inference
Part 3: Observational studies
10. Observational Studies, Selection Bias, and Nonparametric Identification of Causal Effects
11. The Central Role of the Propensity Score in Observational Studies for Causal Effects
12. The Doubly Robust or the Augmented Inverse Propensity Score Weighting Estimator for the Average Causal Effect
13. The Average Causal Effect on the Treated Units and Other Estimands
14. Using the Propensity Score in Regressions for Causal Effects
15. Matching in Observational Studies
Part 4: Difficulties and challenges of observational studies
16. Difficulties of Unconfoundedness in Observational Studies for Causal Effects
17. E-Value: Evidence for Causation in Observational Studies with Unmeasured Confounding
18. Sensitivity Analysis for the Average Causal Effect with Unmeasured Confounding
19. Rosenbaum-Style p-Values for Matched Observational Studies with Unmeasured Confounding
20. Overlap in Observational Studies: Difficulties and Opportunities
Part 5: Instrumental variables
21. An Experimental Perspective of the Instrumental Variable
22. Disentangle Mixture Distributions and Instrumental Variable Inequalities
23. An Econometric Perspective of the Instrumental Variable
24. Application of the Instrumental Variable Method: Fuzzy Regression Discontinuity
25. Application of the Instrumental Variable Method: Mendelian Randomization
Part 6: Causal Mechanisms with Post-Treatment Variables
26. Principal Stratification
27. Mediation Analysis: Natural Direct and Indirect Effects
28. Controlled Direct Effect
29. Time-Varying Treatment and Confounding
Part 7: Appendices
A. Probability and Statistics
B. Linear and Logistic Regressions
C. Some Useful Lemmas for Simple Random Sampling From a Finite Population
Part 1: Introduction
1. Correlation, Association, and the Yule–Simpson Paradox
2. Potential Outcomes
Part 2: Randomized experiments
3. The Completely Randomized Experiment and the Fisher Randomization Test
4. Neymanian Repeated Sampling Inference in Completely Randomized Experiments
5. Stratification and Post-Stratification in Randomized Experiments
6. Rerandomization and Regression Adjustment
7. Matched-Pairs Experiment
8. Unification of the Fisherian and Neymanian Inferences in Randomized Experiments
9. Bridging Finite and Super Population Causal Inference
Part 3: Observational studies
10. Observational Studies, Selection Bias, and Nonparametric Identification of Causal Effects
11. The Central Role of the Propensity Score in Observational Studies for Causal Effects
12. The Doubly Robust or the Augmented Inverse Propensity Score Weighting Estimator for the Average Causal Effect
13. The Average Causal Effect on the Treated Units and Other Estimands
14. Using the Propensity Score in Regressions for Causal Effects
15. Matching in Observational Studies
Part 4: Difficulties and challenges of observational studies
16. Difficulties of Unconfoundedness in Observational Studies for Causal Effects
17. E-Value: Evidence for Causation in Observational Studies with Unmeasured Confounding
18. Sensitivity Analysis for the Average Causal Effect with Unmeasured Confounding
19. Rosenbaum-Style p-Values for Matched Observational Studies with Unmeasured Confounding
20. Overlap in Observational Studies: Difficulties and Opportunities
Part 5: Instrumental variables
21. An Experimental Perspective of the Instrumental Variable
22. Disentangle Mixture Distributions and Instrumental Variable Inequalities
23. An Econometric Perspective of the Instrumental Variable
24. Application of the Instrumental Variable Method: Fuzzy Regression Discontinuity
25. Application of the Instrumental Variable Method: Mendelian Randomization
Part 6: Causal Mechanisms with Post-Treatment Variables
26. Principal Stratification
27. Mediation Analysis: Natural Direct and Indirect Effects
28. Controlled Direct Effect
29. Time-Varying Treatment and Confounding
Part 7: Appendices
A. Probability and Statistics
B. Linear and Logistic Regressions
C. Some Useful Lemmas for Simple Random Sampling From a Finite Population
Notă biografică
Peng Ding is an Associate Professor in the Department of Statistics at UC Berkeley. His research focuses on causal inference and its applications.
Recenzii
"This book offers a statistician’s perspective on causal inference. It provides an invaluable review of statistical paradoxes in causal inference from observational data, linking those paradoxes to Pearl’s directed acyclic graphs (DAGs). The overview of the literature on matching is the best that I’ve 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.
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.
Descriere
This textbook, based on the author's 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.