Design of Observational Studies

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, r [...]
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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 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.