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- W2898042855 abstract "When causal effects are to be estimated from observational data, we have to adjust for confounding. A central aim of covariate selection for causal inference is therefore to determine a set that is sufficient for confounding adjustment, but other aims such as efficiency or robustness can be important as well. In this paper, we review six general approaches to covariate selection that differ in the targeted type of adjustment set. We discuss and illustrate their advantages and disadvantages using causal diagrams. Moreover, the approaches and different ways of implementing them are compared empirically in an extensive simulation study. We conclude that there are considerable differences between the approaches but none of them is uniformly best, with performance depending on the chosen adjustment method as well as the true confounding structure. Any prior structural knowledge on the causal relations is helpful to choose the most appropriate method." @default.
- W2898042855 created "2018-10-26" @default.
- W2898042855 creator A5035468346 @default.
- W2898042855 creator A5038470056 @default.
- W2898042855 date "2018-10-10" @default.
- W2898042855 modified "2023-09-29" @default.
- W2898042855 title "Covariate selection strategies for causal inference: Classification and comparison" @default.
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- W2898042855 doi "https://doi.org/10.1002/bimj.201700294" @default.
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