The strongly ignorable treatment assignment assumption (also known as no unmeasured confounding) requires a sufficiently large set of covariates being measured to ensure that subjects are exchangeable across the observed exposure given measured covariates. Although administrative data are rich in information, key confounders might not be captured. Several Bayesian sensitivity analyses for unmeasured confounding have been developed that use bias parameters to capture the effect of latent confounders on the outcome and exposure. However, there is a lack of considerations to handle time-dependent latent confounders. In this talk, I will present two parametric Bayesian causal approaches to tackle causal estimation when the causal structure features time-dependent latent confounders. We will discuss these methods under different simulated scenarios with varying strength of relations between the latent confounders and the observed variables.