Causal knowledge as a prerequisite for confounding evaluation: an application to birth defects epidemiology.

By Miguel A. Hern'an, S. Hernández-Díaz, M. Werler, A. Mitchell. American Journal of Epidemiology, 2002

1,420 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Rheumatology, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/581e7e0c8e93592755b8f8c1678c27c4cff78ca5

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Citations in the corpus

On model selection and model misspeciﬁcation in causal inference (2010, neutral): “i=1 { 1−Ai 1− π̂(Li) } {Yi − ω̂0(Li)}φ0(Li), (12)”
Causality, mediation and time: a dynamic viewpoint (2012, methodology): “For example in the discussion of various DAGs in Hernán et al. (2002) a collider-type argument is given for why one should not adjust for birth weight when assessing whether low folate intake increases the risk of birth defects.”
On model selection and model misspecification in causal inference (2012, positive): “This is largely because adjustment for covariates that are affected by the exposure or the outcome can actually increase bias, which makes purely associational approaches to confounder-selection fallible.(12) Causal diagrams(7,8,10) are very helpful to communicate and visualise the data-generating m”
Overadjustment Bias and Unnecessary Adjustment in Epidemiologic Studies (2009, background): “A set of rules for causal diagrams are succinctly described by Greenland et al10 and in the appendix of Hernan et al.11 Briefly, causal diagrams link variables by singleheaded (ie, directed) arrows that represent direct causal effects.”
The impact of residual and unmeasured confounding in epidemiologic studies: a simulation study. (2007, background): “Rules such as the change-in-estimate criterion should be applied carefully or not at all (37).”
Can we believe the DAGs? A comment on the relationship between causal DAGs and mechanisms (2014, background): “For example, Herna´n et al. 38 state, ‘‘Causal diagrams are a useful way to summarize, clarify, and communicate one’s qualitative beliefs about the causal structure.’’”
Constructing inverse probability weights for marginal structural models. (2008, methodology): “We do not reiterate the approach to sensitivity analysis here but, rather, assume that the most important confounders were identified using expert knowledge (27, 28) and were then appropriately measured and included in the analysis.”