  Estimating individualized treatment effects (ITEs) from observational data is
crucial for decision-making. In order to obtain unbiased ITE estimates, a
common assumption is that all confounders are observed. However, in practice,
it is unlikely that we observe these confounders directly. Instead, we often
observe noisy measurements of true confounders, which can serve as valid
proxies. In this paper, we address the problem of estimating ITE in the
longitudinal setting where we observe noisy proxies instead of true
confounders. To this end, we develop the Deconfounding Temporal Autoencoder, a
novel method that leverages observed noisy proxies to learn a hidden embedding
that reflects the true hidden confounders. In particular, the DTA combines a
long short-term memory autoencoder with a causal regularization penalty that
renders the potential outcomes and treatment assignment conditionally
independent given the learned hidden embedding. Once the hidden embedding is
learned via DTA, state-of-the-art outcome models can be used to control for it
and obtain unbiased estimates of ITE. Using synthetic and real-world medical
data, we demonstrate the effectiveness of our DTA by improving over
state-of-the-art benchmarks by a substantial margin.
