Handbook of matching and weighting adjustments for causal inference. (2023)

• Record Type: Book
• Further Information: Note: Edited by José Zubizarreta, Elizabeth A. Stuart, Dylan Small, Paul R. Rosenbaum.
• Editors: Zubizarreta, José R; Stuart, Elizabeth A; Rosenbaum, Paul R; Small, Dylan S
• Contents: Part I: Conceptual issues Overview of methods for adjustment and applications in the social and behavioral sciences: The role of study design; Ting-Hsuan Chang and Elizabeth A. Stuart Propensity score; Paul R. Rosenbaum Generalization and Transportability; Elizabeth Tipton and Erin Hartman Part II: Matching Optimization techniques in multivariate matching; Paul R. Rosenbaum and José R. Zubizarreta Optimal Full matching; Mark M. Fredrickson and Ben Hansen Fine balance and its variations in modern optimal matching; Samuel D. Pimentel Matching with instrumental variables; Mike Baiocchi and Hyunseung Kang Covariate Adjustment in Regression Discontinuity Designs; Matias D. Cattaneo, Luke Keele, Rocío Titiunik Risk Set Matching; Bo Lu and Robert A. Greevy, Jr. Matching with Multilevel Data; Samuel D. Pimentel and Luke Keele Effect Modification in Observational Studies; Kwonsang Lee and Jesse Y. Hsu Optimal Nonbipartite Matching; Robert A. Greevy, Jr. and Bo Lu Matching Methods for Large Observational Studies; Ruoqi Yu Part III: Weighting Overlap Weighting; Fan Li Covariate Balancing Propensity Score; Kosuke Imai and Yang Ning Balancing Weights for Causal Inference; Eric R. Cohn, Eli Ben-Michael, Avi Feller, and José R. Zubizarreta Assessing Principal Causal Effects Using Principal Score Methods; Alessandra Mattei, Laura Forastiere, Fabrizia Mealli Incremental Causal Effects: An Introduction and Review; Matteo Bonvini, Alec McClean, Zach Branson and Edward H. Kennedy Weighting Estimators for Causal Mediation; Donna L. Coffman, Megan S. Schuler, Trang Q. Nguyen, and Daniel F. McCaffrey Part IV: Machine Learning Adjustments Machine Learning for Causal Inference; Jennifer Hill, George Perrett and Vincent Dorie Treatment Heterogeneity with Survival Outcomes; Yizhe Xu, Nikolaos Ignatiadis, Erik Sverdrup, Scott Fleming, Stefan Wager, Nigam Shah Why Machine Learning Cannot Ignore Maximum Likelihood Estimation; Mark J. van der Laan and Sherri Rose Bayesian Propensity Score methods and Related Approaches for Confounding Adjustment Joseph Antonelli Part V: Beyond Adjustments How to Be a Good Critic of an Observational Study; Dylan S. Small Sensitivity Analysis; C.B. Fogarty Evidence Factors; Bikram Karmakar
• Edition: 1st
• Publisher Details: Boca Raton : Chapman & Hall/CRC
• Publication Date: 2023
• Extent: 1 online resource (708 pages), illustrations (black and white, and colour)
• Subjects: 001.433; Sampling (Statistics) -- Handbooks, manuals, etc; Research -- Statistical methods -- Handbooks, manuals, etc
• Languages: English
• ISBNs: 9781000850857; 9781000850819
• Related ISBNs: 9780367609528
• Notes: Note: Includes bibliographical references and index.; Note: Description based on CIP data; resource not viewed.
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.