Deconfounding Temporal Autoencoder: Estimating Treatment Effects over
Time Using Noisy Proxies
Deconfounding Temporal Autoencoder: Estimating Treatment Effects over
Time Using Noisy Proxies
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 …