Handbook of Matching and Weighting Adjustments for Causal Inference

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.

一項觀察性研究推斷在無法進行隨機實驗的情況下，治療、政策、計劃、干預或暴露所引起的效應。觀察性研究的一個任務是調整處理組和對照組之間可見的預處理差異。多變量匹配和加權是兩種現代調整方法。本手冊提供了最新的匹配、加權、機器學習及其組合的綜合調查。另外三章介紹了在調整完成後從關聯到因果的步驟。

當單獨使用匹配和加權時，它們不使用結果信息，因此它們是觀察性研究設計的一部分。當與結果模型一起使用時，匹配和加權可以增強基於模型的調整的穩健性。本書適用於醫學、經濟學、公共衛生、心理學、流行病學、公共計劃評估和統計學等領域的研究人員，他們研究治療、政策或暴露對人類的影響的證據。

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 University's 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.

José Zubizarreta博士是哈佛醫學院醫療政策系和哈佛大學生物統計系的副教授。他是美國統計學會的會士，並獲得了Kenneth Rothman獎、William Cochran獎和Tom Ten Have紀念獎。

Elizabeth A. Stuart博士是約翰霍普金斯大學布隆伯格公共衛生學院心理健康系、生物統計學系和衛生政策與管理系的布隆伯格教授。她是美國統計學會的會士，並獲得了美國統計學會衛生政策統計部門的中期職業獎、應用統計學的Gertrude Cox獎、哈佛大學生物統計學卓越獎和流行病學研究學會Marshall Joffe流行病學方法獎。

Dylan Small博士是賓夕法尼亞大學沃頓商學院統計與數據科學系的Universal Furniture教授。他是美國統計學會的會士和數學統計學會的Medallion演講者。

Paul R. Rosenbaum是賓夕法尼亞大學沃頓商學院統計與數據科學系的名譽教授。他獲得了統計學會主席委員會頒發的R. A. Fisher獎和George W. Snedecor獎。他是多本書的作者，包括《觀察研究設計》和《觀察研究中的複製和證據因素》。