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Abstract:
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Estimating average causal effects from observational data generally requires adjustment for confounding. Instrumental variable (IV) estimation is being increasingly used for confounding adjustment in observational research, with some common proposed instruments being calendar period, genetic traits, and physician's preference. The validity of these IV estimates requires strong conditions that are unlikely to hold, even approximately, in many applications. This talk will discuss some key shortcomings, including some that are rarely discussed, of IV estimation for causal inference from observational research, explore IV approaches that are more robust to violations of assumptions, and describe reporting guidelines for IV analyses.
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