Diagnosing Bias in the Inverse Probability of Treatment Weighted Estimator Resulting from Violation of Experimental Treatment Assignment

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Estimation and extrapolation of optimal treatment and testing strategies (2008, methodology): “Problems with large or even truly infinite weights (when positivity does not hold) can be somewhat ameliorated but not cured, by using bounded doubly robust estimators [12], adjusting for baseline covariates and then using the covariates in the numerator of the weights, downweighting or eliminating ”
Constructing inverse probability weights for marginal structural models. (2008, background): “(33) have proposed a computationally demanding diagnostic tool to quantify the finite-sample bias due to random zeros in weighted estimates.”
Diagnosing and responding to violations in the positivity assumption (2010, unknown): “assumption (7) is violated), some of the conditional probabilities in the non-parametric G-computation formula (5) will not be defined.”
Comparison of Approaches to Weight Truncation for Marginal Structural Cox Models (2013, methodology): “These include (i) truncation, whereby all the weights larger than the user-specified maximum weight are replaced by that threshold value (Kish, 1992; Wang et al., 2006; Bembom and van der Laan, 2008b; Cole and Hernán, 2008; Moore et al., 2009), (ii) use of non-IPTW estimators, such as G-computation ”
Estimation of controlled direct effects (2008, unknown): “Alternatively, one could truncate the weights (Wang et al., 2006).”