- Main Menu-

Catalogue Tag Display

MARC 21

Statistical Causal Inferences and Their Applications in Public Health Research
Tag	Description
020	$a9783319412597$9978-3-319-41259-7
082	$a519.5$223
099	$aOnline resource: Springer
245	$aStatistical Causal Inferences and Their Applications in Public Health Research$h[EBook]$cedited by Hua He, Pan Wu, Ding-Geng (Din) Chen.
260	$aCham :$bSpringer International Publishing :$bImprint: Springer,$c2016.
300	$aXV, 321 p. 24 illus., 11 illus. in color.$bonline resource.
336	$atext$btxt$2rdacontent
337	$acomputer$bc$2rdamedia
338	$aonline resource$bcr$2rdacarrier
440	$aICSA Book Series in Statistics,$x2199-0980
505	$aPart I. Overview -- 1. Causal Inference – A Statistical Paradigm for Inferring Causality -- Part II. Propensity Score Method for Causal Inference -- 2. Overview of Propensity Score Methods -- 3. Sufficient Covariate, Propensity Variable and Doubly Robust Estimation -- 4. A Robustness Index of Propensity Score Estimation to Uncontrolled Confounders -- 5. Missing Confounder Data in Propensity Score Methods for Causal Inference -- 6. Propensity Score Modeling & Evaluation -- 7. Overcoming the Computing Barriers in Statistical Causal Inference -- Part III. Causal Inference in Randomized Clinical Studies -- 8. Semiparametric Theory and Empirical Processes in Causal Inference -- 9. Structural Nested Models for Cluster-Randomized Trials -- 10. Causal Models for Randomized Trials with Continuous Compliance -- 11. Causal Ensembles for Evaluating the Effect of Delayed Switch to Second-line Antiretroviral Regimens -- 12. Structural Functional Response Models for Complex Intervention Trials -- Part IV. Structural Equation Models for Mediation Analysis -- 13.Identification of Causal Mediation Models with An Unobserved Pre-treatment Confounder -- 14. A Comparison of Potential Outcome Approaches for Assessing Causal Mediation -- 15. Causal Mediation Analysis Using Structure Equation Models. .
520	$aThis book compiles and presents new developments in statistical causal inference. The accompanying data and computer programs are publicly available so readers may replicate the model development and data analysis presented in each chapter. In this way, methodology is taught so that readers may implement it directly. The book brings together experts engaged in causal inference research to present and discuss recent issues in causal inference methodological development. This is also a timely look at causal inference applied to scenarios that range from clinical trials to mediation and public health research more broadly. In an academic setting, this book will serve as a reference and guide to a course in causal inference at the graduate level (Master's or Doctorate). It is particularly relevant for students pursuing degrees in Statistics, Biostatistics and Computational Biology. Researchers and data analysts in public health and biomedical research will also find this book to be an important reference.
538	$aOnline access to this digital book is restricted to subscription institutions through IP address (only for SISSA internal users)
700	$aHe, Hua.$eeditor.
700	$aWu, Pan.$eeditor.
700	$aChen, Ding-Geng (Din).$eeditor.
710	$aSpringerLink (Online service)
830	$aICSA Book Series in Statistics,$x2199-0980
856	$uhttp://dx.doi.org/10.1007/978-3-319-41259-7