Fundamentals of causal inference : with R
Author(s)
Bibliographic Information
Fundamentals of causal inference : with R
（Texts in statistical science）
CRC Press, 2022
- : hbk
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Note
Includes bibliographical references (p. 225-231) and index
Description and Table of Contents
Description
Requires minimal prerequisites
Explained in basic terms
Illustrated with binary datasets and real life examples
Covers primary concepts and methods
Accessible to undergraduates
Suitable for a heterogeneous audience
Table of Contents
1. Introduction. 2. Conditional Probability and Expectation. 3. Potential Outcomes and the Fundamental Problem of Causal Inference. 4. Effect-measure Modification and Causal Interaction. 5. Causal Directed Acyclic Graphs. 6. Adjusting for Confounding: Back-door method via Standardization. 7. Adjusting for Confounding: Difference-in-Differences Estimators. 8. Adjusting for Confounding: Front-door method. 9. Adjusting for Confounding: Instrumental Variables. 10. Adjusting for Confounding: Propensity-score Methods. 11. Efficiency with Precision Variables. 12. Mediation.
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