One of the primary motivations for clinical trials and observational studies of humans is to infer cause and effect. Disentangling causation from confounding is of utmost importance. Fundamentals of Causal Inference explains and relates different methods of confounding adjustment in terms of potenti...

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1. Verfasser:	Brumback, Babette A. (VerfasserIn)
Format:	Buch
Sprache:	English
Veröffentlicht:	Boca Raton ; London ; New York CRC Press, Taylor & Francis Group 2022
Ausgabe:	First edition
Schriftenreihe:	Chapman & Hall/CRC Texts in Statistical Science Series
Zusammenfassung:	One of the primary motivations for clinical trials and observational studies of humans is to infer cause and effect. Disentangling causation from confounding is of utmost importance. Fundamentals of Causal Inference explains and relates different methods of confounding adjustment in terms of potential outcomes and graphical models, including standardization, difference-in-differences estimation, the front-door method, instrumental variables estimation, and propensity score methods. It also covers effect-measure modification, precision variables, mediation analyses, and time-dependent confounding. Several real data examples, simulation studies, and analyses using R motivate the methods throughout. The book assumes familiarity with basic statistics and probability, regression, and R and is suitable for seniors or graduate students in statistics, biostatistics, and data science as well as PhD students in a wide variety of other disciplines, including epidemiology, pharmacy, the health sciences, education, and the social, economic, and behavioral sciences. Beginning with a brief history and a review of essential elements of probability and statistics, a unique feature of the book is its focus on real and simulated datasets with all binary variables to reduce complex methods down to their fundamentals. Calculus is not required, but a willingness to tackle mathematical notation, difficult concepts, and intricate logical arguments is essential. While many real data examples are included, the book also features the Double What-If Study, based on simulated data with known causal mechanisms, in the belief that the methods are best understood in circumstances where they are known to either succeed or fail. Datasets, R code, and solutions to odd-numbered exercises are available at www.routledge.com
Beschreibung:	1. Introduction ; A Brief History ; Data Examples ; Mortality rates by country ; National Center for Education Statistics ; Reducing Alcohol Consumption ; The What-If? Study ; The Double What-If? Study ; General Social Survey ; A Cancer Clinical Trial ; Exercises ; 2. Conditional Probability and Expectation ; Conditional Probability ; Conditional Expectation and the Law of Total Expectation ; Estimation ; Sampling Distributions and the Bootstrap ; Exercises ; 3. Potential Outcomes and the Fundamental Problem of Causal Inference ; Potential Outcomes and the Consistency Assumption ; Circumventing the Fundamental Problem of Causal Inference ; Exercises ; 4. Effect-Measure Modification and Causal Interaction ; Effect-Measure Modification and Statistical Interaction; Qualitative Agreement of Effect Measures in Modification ; Causal Interaction ; Exercises ; Contents; 5. Causal Directed Acyclic Graphs ; Theory ; Examples ; Exercises ; 6. - Adjusting for Confounding: Backdoor Method via Standardization ; Standardization via Outcome Modeling ; Average Effect of Treatment on the Treated ; Standardization with a Parametric Outcome Model ; Standardization via Exposure Modeling ; Average Effect of Treatment on the Treated ; Standardization with a Parametric Exposure Model ; Doubly Robust Standardization ; Exercises ; 7. Adjusting for Confounding: Difference-in-Differences Estimators ; Difference-in-Differences (DiD) Estimators with Linear, Loglinear, and Logistic Models ; DiD Estimator Estimator with a Linear Model ; DiD Estimator with a Loglinear Model ; DiD Estimator with a Logistic Model ; Comparison with Standardization ; Exercises ; 8. Adjusting for Confounding: Front-Door Method ; Motivation ; Theory and Method ; Simulated Example ; Exercises ; 9. - Adjusting for Confounding: Instrumental Variables ; Complier Average Causal Effect and Principal Stratification ; Average Effect of Treatment on the Treated and Structural; Nested Mean Models ; Examples ; Exercises ; 10. Adjusting for Confounding: Propensity-Score Methods ; Theory ; Using the Propensity Score in the Outcome Model ; Stratification on the Propensity Score ; Matching on the Propensity Score ; Exercises ; Contents ix; 11. Gaining Efficiency with Precision Variables ; Theory ; Examples ; Exercises ; 12. Mediation ; Theory ; Traditional Parametric Methods ; More Examples ; Exercise ; Adjusting for Time-Dependent Confounding ; Marginal Structural Models ; Structural Nested Mean Models ; Optimal Dynamic Treatment Regimes ; Exercises ; Appendix Bibliography Index
Beschreibung:	xii, 236 Seiten Illustrationen 234 mm
ISBN:	9780367705053 9780367705091