Mediation analysis in epidemiology: methods, interpretation and bias.

By L. Richiardi, R. Bellocco, D. Zugna. International Journal of Epidemiology, 2013

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Applying the Bradford Hill criteria in the 21st century: how data integration has changed causal inference in molecular epidemiology (2015, background): “For example, the biostatistical approach of mediation analysis allows for the disentanglement and decomposition of the various biological pathways of direct and indirect effects that play a role in filling the “black box” between exposures and observable outcomes [47].”
Mendelian randomisation for mediation analysis: current methods and challenges for implementation (2019, neutral): “2a) and (iii) no exposure-mediator interaction [4, 6, 7].”
Interpretation and identification of causal mediation. (2014, neutral): “Recently, efforts have been made to interpret these conditions in more conceptually meaningful way, so as to enable researchers to judge whether the necessary assumptions are scientifically plausible (Coffman and Zhong, 2012; Imai et al., 2010a, 2011a; Muthén, 2011; Richiardi et al., 2013; Valeri an”
Unbiased Scene Graph Generation From Biased Training (2020, neutral): “It is also known as effect analysis [57, 41], which is widely adopted in medical, political or psychological research [45, 19, 8, 33, 21] as the tool of studying the effect of certain treatments or policies.”
Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect (2020, neutral): “Causal inference [23, 35] has been widely adopted in psychology, politics and epidemiology for years [36–38].”