Fundamentals of Causal Inference with R 1st Edition by Babette A Brumback ISBN 9780367705053 0367705052

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Product details:
ISBN 10: 0367705052
ISBN 13: 9780367705053
Author: Babette A Brumback

Fundamentals of Causal Inference with R 1st Edition Table of contents:

1 Introduction

1.1 A Brief History

1.2 Data Examples

1.2.1 Mortality Rates by Country

1.2.2 National Center for Education Statistics

1.2.3 Reducing Alcohol Consumption

1.2.3.1 The What-If? Study

1.2.3.2 The Double What-If? Study

1.2.4 General Social Survey

1.2.5 A Cancer Clinical Trial

1.3 Exercises

2 Conditional Probability and Expectation

2.1 Conditional Probability

2.2 Conditional Expectation and the Law of Total Expectation

2.3 Estimation

2.4 Sampling Distributions and the Bootstrap

2.5 Exercises

3 Potential Outcomes and the Fundamental Problem of Causal Inference

3.1 Potential Outcomes and the Consistency Assumption

3.2 Circumventing the Fundamental Problem of Causal Inference

3.3 Effect Measures

3.4 Exercises

4 Effect-Measure Modification and Causal Interaction

4.1 Effect-Measure Modification and Statistical Interaction

4.2 Qualitative Agreement of Effect Measures in Modification

4.3 Causal Interaction

4.4 Exercises

5 Causal Directed Acyclic Graphs

5.1 Theory

5.2 Examples

5.3 Exercises

6 Adjusting for Confounding: Backdoor Method via Standardization

6.1 Standardization via Outcome Modeling

6.1.1 Average Effect of Treatment on the Treated

6.1.2 Standardization with a Parametric Outcome Model

6.2 Standardization via Exposure Modeling

6.2.1 Average Effect of Treatment on the Treated

6.2.2 Standardization with a Parametric Exposure Model

6.3 Doubly Robust Standardization

6.4 Exercises

7 Adjusting for Confounding: Difference-in-Differences Estimators

7.1 Difference-in-Differences (DiD) Estimators with Linear, Loglinear, and Logistic Models

7.1.1 DiD Estimator with a Linear Model

7.1.2 DiD Estimator with a Loglinear Model

7.1.3 DiD Estimator with a Logistic Model

7.2 Comparison with Standardization

7.3 Exercises

8 Adjusting for Confounding: Front-Door Method

8.1 Motivation

8.2 Theory and Method

8.3 Simulated Example

8.4 Exercises

9 Adjusting for Confounding: Instrumental Variables

9.1 Complier Average Causal Effect and Principal Stratification

9.2 Average Effect of Treatment on the Treated and Structural Nested Mean Models

9.3 Examples

9.4 Exercises

10 Adjusting for Confounding: Propensity-Score Methods

10.1 Theory

10.2 Using the Propensity Score in the Outcome Model

10.3 Stratification on the Propensity Score

10.4 Matching on the Propensity Score

10.5 Exercises

11 Gaining Efficiency with Precision Variables

11.1 Theory

11.2 Examples

11.3 Exercises

12 Mediation

12.1 Theory

12.2 Traditional Parametric Methods

12.3 More Examples

12.4 Exercise

13 Adjusting for Time-Dependent Confounding

13.1 Marginal Structural Models

13.2 Structural Nested Mean Models

13.3 Optimal Dynamic Treatment Regimes

13.4 Exercises

Appendix

Bibliography

Index

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Tags: Babette A Brumback, Fundamentals, Causal Inference, R