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Design of Observational Studies

Paul R. Rosenbaum

978-1-4419-1213-8

2010
Edition 1
- 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
- Includes supplementary material: sn.pub/extras
€220

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