Correcting for misclassification in two-way tables and matched-pair studies.

By Sander Greenland, D. Kleinbaum. 1983

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Causal Effect Inference with Deep Latent-Variable Models (2017, neutral): “Recent work by Cai and Kuroki [9], Greenland and Lash [18], building on the work of Greenland and Kleinbaum [17], Selén [47], has studied conditions for causal identifiability using proxy variables.”
The Analysis and Interpretation of Multivariate Data for Social Scientists (2002, methodology): “Use extreme care when applying these methods to case-control data with disease misclassification; due to the oversampling of apparent cases, misclassification rates in the study differ substantially from those found in the general population (Greenland and Kleinbaum, 1983).”
On Measurement Bias in Causal Inference (2010, background): “It is motivated by a result known to epidemiologists (Greenland and Kleinbaum, 1983; Greenland and Lash, 2008) and regression analysts (Carroll et al., 2006; Selén, 1986) that has not been fully utilized in causal analysis or graphical models.”