  Causal inference for observational longitudinal studies often requires the
accurate estimation of treatment effects on time-to-event outcomes in the
presence of time-dependent patient history and time-dependent covariates. To
tackle this longitudinal treatment effect estimation problem, we have developed
a time-variant causal survival (TCS) model that uses the potential outcomes
framework with an ensemble of recurrent subnetworks to estimate the difference
in survival probabilities and its confidence interval over time as a function
of time-dependent covariates and treatments. Using simulated survival datasets,
the TCS model showed good causal effect estimation performance across scenarios
of varying sample dimensions, event rates, confounding and overlapping.
However, increasing the sample size was not effective in alleviating the
adverse impact of a high level of confounding. In a large clinical cohort
study, TCS identified the expected conditional average treatment effect and
detected individual treatment effect heterogeneity over time. TCS provides an
efficient way to estimate and update individualized treatment effects over
time, in order to improve clinical decisions. The use of a propensity score
layer and potential outcome subnetworks helps correcting for selection bias.
However, the proposed model is limited in its ability to correct the bias from
unmeasured confounding, and more extensive testing of TCS under extreme
scenarios such as low overlapping and the presence of unmeasured confounders is
desired and left for future work.
