Adjustment uncertainty in effect estimation

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On model selection and model misspecification in causal inference (2012, unknown): “(b) Estimates of the MLOR and CLOR as obtained through FIC-based covariate adjustment, and through AIC-based selection as in Crainiceanu et al.(3) 14 Statistical Methods in Medical Research 21(1)”
Globally efficient non-parametric inference of average treatment effects by empirical balancing calibration weighting (2015, methodology): “Since the parameter K controls the number of moment conditions for matching to eliminate the bias from confounding, the choice of K is therefore analogous to the selection of the number of confounders in regression modelling, for which a graphical method was discussed in Crainiceanu et al. (2008).”
Distributed lag non-linear models (2010, background): “This problem, sometimes referred to as adjustment uncertainty, has received some attention in time series studies of pollution [29, 30] as well as generally [31].”
On model selection and model misspeciﬁcation in causal inference (2010, unknown): “It is easily seen from both bias expressions (3) and (4) that any degree of model misspecification in the propensity score of magnitude δ(L) = π∗(L)− π(L) at a given L, and in the outcome regression models of magnitudes ∆1(L) = ω∗ 0(L) + τ ∗ − ω1(L) and ∆0(L) = ω∗ 0(L) − ω0(L) yields a contribution ”