Covariate selection for the nonparametric estimation of an average treatment effect

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Graphical criteria for efficient total effect estimation via adjustment in causal linear models (2019, methodology): “, 2008; Hauser and Bühlmann, 2012; Eigenmann et al., 2017; Wang et al., 2017). In the presence of hidden variables, the counterparts of DAGs and CPDAGs are maximal ancestral graphs (MAGs) and partial ancestral graphs (PAGs). The back-door criterion was generalized to CPDAGs, MAGs and PAGs by Maathui”
Do-calculus when the True Graph Is Unknown (2015, methodology): “More data-driven methods to ﬁnd adjustment sets have also been developed (De Luna et al., 2011; Entner et al., 2013; VanderWeele and Shpitser, 2011).”