  Observational data have been actively used to estimate treatment effect,
driven by the growing availability of electronic health records (EHRs).
However, EHRs typically consist of longitudinal records, often introducing
time-dependent confoundings that hinder the unbiased estimation of treatment
effect. Inverse probability of treatment weighting (IPTW) is a widely used
propensity score method since it provides unbiased treatment effect estimation
and its derivation is straightforward. In this study, we aim to utilize IPTW to
estimate treatment effect in the presence of time-dependent confounding using
claims records. Previous studies have utilized propensity score methods with
features derived from claims records through feature processing, which
generally requires domain knowledge and additional resources to extract
information to accurately estimate propensity scores. Deep sequence models,
particularly recurrent neural networks and self-attention-based architectures,
have demonstrated good performance in modeling EHRs for various downstream
tasks. We propose that these deep sequence models can provide accurate IPTW
estimation of treatment effect by directly estimating the propensity scores
from claims records without the need for feature processing. We empirically
demonstrate this by conducting comprehensive evaluations using synthetic and
semi-synthetic datasets.
