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03927aam a2200457 i 4500 001 1C23B754440211EF98CC15ED37ECA4DB 003 SILO 005 20240717010108 008 230103t20232023flua bf 001 0 eng 010 $a 2022045526 020 $a 0367609533 020 $a 9780367609535 020 $a 0367609525 020 $a 9780367609528 035 $a (OCoLC)1346352142 040 $a LBSOR $b eng $e rda $c DLC $d BDX $d UKMGB $d OCLCF $d YDX $d SILO 042 $a pcc 050 00 $a HA31.2 $b .H33 2023 082 00 $a 001.4/33 $2 23/eng/20230419 245 00 $a Handbook of matching and weighting adjustments for causal inference / $c edited by Jose R. Zubizarreta, Elizabeth A. Stuart, Dylan S. Small, Paul R. Rosenbaum. 250 $a First edition. 264 1 $a Boca Raton : $b CRC Press, Taylor & Francis Group, $c 2023. 300 $a xi, 621 pages : $b illustrations (some color) ; $c 27 cm 490 1 $a Handbooks of modern statistical methods 504 $a Includes bibliographical references and index. 505 0 $a Overview of methods for adjustment -- The propensity score -- Generalization and transportation -- Optimization techniques in multivariate matching -- Full matching -- Fine balance -- Matching with instrumental variables -- Matching in discontinuity designs -- Risk set matching and isolation -- Multilevel matching -- Matching to study effect modification -- Nonbipartite matching -- Large scale network matching -- Overlap weights -- Covariate balancing propensity score -- Balancing weights -- Principal scores -- Incremental interventions -- Matching and weighting adjustments for mediation analysis -- Bayesian Additive Regression Trees -- Treatment effect heterogeneity -- Targeted learning -- Bayesian propensity score methods -- Being a good critic of an observational study -- Sensitivity analysis -- Evidence factors. 520 $a "An observational study infers the effects caused by a treatment, policy, program, intervention, or exposure in a context in which randomized experimentation is unethical or impractical. One task in an observational study is to adjust for visible pretreatment differences between the treated and control groups. Multivariate matching and weighting are two modern forms of adjustment. This handbook provides a comprehensive survey of the most recent methods of adjustment by matching, weighting, machine learning and their combinations. Three additional chapters introduce the steps from association to causation that follow after adjustments are complete. When used alone, matching and weighting do not use outcome information, so they are part of the design of an observational study. When used in conjunction with models for the outcome, matching and weighting may enhance the robustness of model-based adjustments. The book is for researchers in medicine, economics, public health, psychology, epidemiology, public program evaluation, and statistics who examine evidence of the effects on human beings of treatments, policies or exposures"-- $c Provided by publisher. 650 0 $a Sampling (Statistics) $v Handbooks, manuals, etc. 650 0 $a Research $x Statistical methods $v Handbooks, manuals, etc. 650 7 $a Research $x Statistical methods. $2 fast $0 (OCoLC)fst01095242 650 7 $a Sampling (Statistics) $2 fast $0 (OCoLC)fst01104676 655 7 $a Handbooks and manuals. $2 fast $0 (OCoLC)fst01423877 700 1 $a Zubizarreta, Jose R., $e editor. 700 1 $a Stuart, Elizabeth A., $e editor. 700 1 $a Small, Dylan S., $e editor. 700 1 $a Rosenbaum, Paul R., $e editor. 776 08 $i Online version: $t Handbook of matching and weighting adjustments for causal inference $d Boca Raton : CRC Press, 2023 $z 9781003102670 $w (DLC) 2022045527 830 0 $a Chapman & Hall/CRC handbooks of modern statistical methods 941 $a 1 952 $l OVUX522 $d 20240717014133.0 956 $a http://locator.silo.lib.ia.us/search.cgi?index_0=id&term_0=1C23B754440211EF98CC15ED37ECA4DBInitiate Another SILO Locator Search