Multivariate matching and weighting are two modern forms of adjustment. 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.
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.
| Contributors |
|
ix | |
| About the Editors |
|
xi | |
|
|
|
1 | (60) |
|
1 Overview of Methods for Adjustment and Applications in the Social and Behavioral Sciences: The Role of Study Design |
|
|
3 | (18) |
|
|
|
|
|
|
|
21 | (18) |
|
|
|
3 Generalizability and Transportability |
|
|
39 | (22) |
|
|
|
|
|
|
|
61 | (200) |
|
4 Optimization Techniques in Multivariate Matching |
|
|
63 | (24) |
|
|
|
|
|
|
|
87 | (18) |
|
|
|
|
|
6 Fine Balance and Its Variations in Modern Optimal Matching |
|
|
105 | (30) |
|
|
|
7 Matching with Instrumental Variables |
|
|
135 | (18) |
|
|
|
|
|
8 Covariate Adjustment in Regression Discontinuity Designs |
|
|
153 | (16) |
|
|
|
|
|
|
|
|
|
169 | (16) |
|
|
|
|
|
10 Matching with Multilevel Data |
|
|
185 | (20) |
|
|
|
|
|
11 Effect Modification in Observational Studies |
|
|
205 | (22) |
|
|
|
|
|
12 Optimal Nonbipartite Matching |
|
|
227 | (12) |
|
|
|
|
|
13 Matching Methods for Large Observational Studies |
|
|
239 | (22) |
|
|
|
|
|
261 | (152) |
|
|
|
263 | (20) |
|
|
|
15 Covariate Balancing Propensity Score |
|
|
283 | (10) |
|
|
|
|
|
16 Balancing Weights for Causal Inference |
|
|
293 | (20) |
|
|
|
|
|
|
|
|
|
17 Assessing Principal Causal Effects Using Principal Score Methods |
|
|
313 | (36) |
|
|
|
|
|
|
|
18 Incremental Causal Effects: An Introduction and Review |
|
|
349 | (24) |
|
|
|
|
|
|
|
|
|
19 Weighting Estimators for Causal Mediation |
|
|
373 | (40) |
|
|
|
|
|
|
|
|
|
IV Outcome Models, Machine Learning and Related Approaches |
|
|
413 | (116) |
|
20 Machine Learning for Causal Inference |
|
|
415 | (30) |
|
|
|
|
|
|
|
21 Treatment Heterogeneity with Survival Outcomes |
|
|
445 | (38) |
|
|
|
|
|
|
|
|
|
|
|
|
|
22 Why Machine Learning Cannot Ignore Maximum Likelihood Estimation |
|
|
483 | (18) |
|
|
|
|
|
23 Bayesian Propensity Score Methods and Related Approaches for Confounding Adjustment |
|
|
501 | (28) |
|
|
|
|
|
529 | (82) |
|
24 How to Be a Good Critic of an Observational Study |
|
|
531 | (22) |
|
|
|
|
|
553 | (30) |
|
|
|
|
|
583 | (28) |
|
|
| Index |
|
611 | |
José Zubizarreta, PhD, is an associate professor in the Department of Health Care Policy at Harvard Medical School and in the Department Biostatistics at Harvard University. He is a Fellow of the American Statistical Association, and is a recipient of the Kenneth Rothman Award, the William Cochran Award, and the Tom Ten Have Memorial Award.
Elizabeth A. Stuart, Ph.D. is Bloomberg Professor of American Health in the Department of Mental Health, the Department of Biostatistics and the Department of Health Policy and Management at Johns Hopkins Bloomberg School of Public Health. She is a Fellow of the American Statistical Association, and she received the mid-career award from the Health Policy Statistics Section of the ASA, the Gertrude Cox Award for applied statistics, Harvard Universitys Myrto Lefkopoulou Award for excellence in Biostatistics, and the Society for Epidemiologic Research Marshall Joffe Epidemiologic Methods award.
Dylan Small, PhD is the Universal Furniture Professor in the Department of Statistics and Data Science of the Wharton School of the University of Pennsylvania. He is a Fellow of the American Statistical Association and an Institute of Mathematical Statistics Medallion Lecturer.
Paul R. Rosenbaum is emeritus professor of Statistics and Data Science at the Wharton School of the University of Pennsylvania. From the Committee of Presidents of Statistical Societies, he received the R. A. Fisher Award and the George W. Snedecor Award. He is the author of several books, including Design of Observational Studies and Replication and Evidence Factors in Observational Studies.
More info about Taylor & Francis e-books