Introduction.- Matching to control bias from measured covariates.- Addressing bias from covariates that were not measured.
Beginnings.- Dilemmas and Craftsmanship.- Causal Inference in Randomized Experiments.- Two Simple Models for Observational Studies.- Competing Theories Structure Design.- Opportunities, Devices, and Instruments.- Transparency.- Matching.- A Matched Observational Study.- Basic Tools of Multivariate Matching.- Various Practical Issues in Matching.- Fine Balance.- Matching Without Groups.- Risk-Set Matching.- Matching in R.- Design Sensitivity.- The Power of a Sensitivity Analysis and Its Limit.- Heterogeneity and Causality.- Uncommon but Dramatic Responses to Treatment.- Anticipated Patterns of Response.- Planning Analysis.- After Matching, Before Analysis.- Planning the Analysis.
An observational study is an empiric investigation of effects caused by treatments when randomized experimentation is unethical or infeasible. Observational studies are common in most fields that study the effects of treatments on people, including medicine, economics, epidemiology, education, psychology, political science and sociology. The quality and strength of evidence provided by an observational study is determined largely by its design. Design of Observational Studies is both an introduction to statistical inference in observational studies and a detailed discussion of the principles that guide the design of observational studies.
Design of Observational Studies is divided into four parts. Chapters 2, 3, and 5 of Part I cover concisely, in about one hundred pages, many of the ideas discussed in Rosenbaum's Observational Studies (also published by Springer) but in a less technical fashion. Part II discusses the practical aspects of using propensity scores and other tools to create a matched comparison that balances many covariates. Part II includes a chapter on matching in R. In Part III, the concept of design sensitivity is used to appraise the relative ability of competing designs to distinguish treatment effects from biases due to unmeasured covariates. Part IV discusses planning the analysis of an observational study, with particular reference to Sir Ronald Fisher's striking advice for observational studies, "make your theories elaborate."
The second edition of his book, Observational Studies, was published by Springer in 2002.
The concepts of causal inference in experiments and observational studies are introduced using the elementary mathematics of independent coin flips to determine treatment assignment
The basic tools of multivariate matching - such as propensity scores, optimal matching, full matching, fine balance, risk set matching - are introduced with many examples and with reference to implementation in R
The key source of uncertainty in an observational study is possible bias from covariates that were not measured. The ability of competing designs to separate treatment effects from unmeasured biases - that is, the design sensitivity - is discussed in detail for the first time in book form