Instruments for Causal Inference: An Epidemiologist's Dream?

By Miguel A. Hern'an, James M. Robins. 2006

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Citations in the corpus

Causal Models for Dynamical Systems (2020, background): “…on the identiﬁcation of causal eﬀects and on causal discovery [e.g. Spirtes et al., 2000, Pearl, 2009, Shpitser, 2018, Richardson and Spirtes, 2002, Hern´an and Robins, 2006, Evans, 2015, Richardson et al., 2017, Hyttinen et al., 2012, Verma and Pearl, 1991, Silva et al., 2006], more work is…”
Observational data for comparative effectiveness research: an emulation of randomised trials to estimate the effect of statins on primary prevention of coronary heart disease (2011, methodology): “Another analytic method that might be have been used to adjust for confounding by indication is instrumental variable (IV) estimation.(19,39) If an instrument is available and certain assumptions hold, one can adjust for both measured and unmeasured confounding.”
An Extended Class of Instrumental Variables for the Estimation of Causal Effects (2011, background): “Note that here, as in Hirano and Imbens (2004), treatments need not be binary.”
Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors (2015, background): “the variant can only affect the outcome through the risk factor—if the value of the genetic variant changes, but not that of the risk factor, then the outcome is unchanged [18].”
Multi-cause causal inference with unmeasured confounding and binary outcome. (2019, background): “Classical approaches to mitigate bias due to unmeasured confounding include instrumental variable methods (e.g., Angrist et al., 1996; Hernán and Robins, 2006; Wang and Tchetgen Tchetgen, 2018), causal structure learning (e.g., Drton and Maathuis, 2017), invariant prediction (e.g., Peters et al.,…”
Statistical inference in two-sample summary-data Mendelian randomization using robust adjusted profile score (2018, background): “The first criterion is considered satisfied if the SNP is correlated with the actual causal SNP [29].”
Marginal and Nested Structural Models Using Instrumental Variables (2010, background): “See Hernan and Robins (2006) for a detailed discussion of homogeneity assumptions and identification issues.”
An examination of multivariable Mendelian randomization in the single-sample and two-sample summary data settings (2018, background): “The requirement for and issues surrounding this assumption are detailed elsewhere.(31, 32)”