Discussion
SECONDARY
By design, both the unmeasured confounder In scenario 1 & 2, the unmeasured confounder After adding an additional relationship between Although the same predictors were used for modeling, the g-computation had the smallest RSMEs in most of settings of this simulation study (Table TMLE is a doubly robust maximum-likelihood-based approach that includes a secondary "targeting" step that optimizes the bias-variance tradeoff for the target parameter [Among the three PS-based weighting methods, OW had the best performance in this simulation. Both IPTW and SMR use the propensity score as a part of denominator for calculating the weight, but OW does not. The weight using reciprocal of propensity score could be greatly amplified when the propensity score is very small or large (e.g. 0.01 or 0.99). Therefore, OW may be less sensitive to the extreme values of propensity score, which might lead to a smaller RSME for estimating the treatment effect compared with IPTW and SMR [One of merits in this study is that we explored some scenarios which the previous studies had not investigated, such as three types of outcomes along with different confounding effects (small and blocked confounding paths, medium and blocked confounding paths, and one large unblocked confounding path) caused by an unmeasured confounder [