  Estimating treatment effects plays a crucial role in causal inference, having
many real-world applications like policy analysis and decision making.
Nevertheless, estimating treatment effects in the longitudinal setting in the
presence of hidden confounders remains an extremely challenging problem.
Recently, there is a growing body of work attempting to obtain unbiased ITE
estimates from time-dynamic observational data by ignoring the possible
existence of hidden confounders. Additionally, many existing works handling
hidden confounders are not applicable for continuous-time settings. In this
paper, we extend the line of work focusing on deconfounding in the dynamic time
setting in the presence of hidden confounders. We leverage recent advancements
in neural differential equations to build a latent factor model using a
stochastic controlled differential equation and Lipschitz constrained
convolutional operation in order to continuously incorporate information about
ongoing interventions and irregularly sampled observations. Experiments on both
synthetic and real-world datasets highlight the promise of continuous time
methods for estimating treatment effects in the presence of hidden confounders.
