Peng Ding

A First Course in Causal Inference (eBook, PDF)

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 448
• Erscheinungstermin: 31. Juli 2024
• Englisch
• ISBN-13: 9781040037133
• Artikelnr.: 72247051

Autorenporträt

Peng Ding is an Associate Professor in the Department of Statistics at UC Berkeley. His research focuses on causal inference and its applications.

Inhaltsangabe

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

Rezensionen

"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.