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E-grāmata: First Course in Causal Inference

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(University of California Berkeley, U.S.A)
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First Course in Causal InferencePalielināt
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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.



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 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, 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.

Recenzijas

"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.

Preface

Part 1: Introduction

1. Correlation, Association, and the YuleSimpson 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
Peng Ding is an Associate Professor in the Department of Statistics at UC Berkeley. His research focuses on causal inference and its applications.
Biežāk uzdotie jautājumi par e-grāmatām Biežāk uzdotie jautājumi par e-grāmatām
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