  Estimating treatment effects over time is relevant in many real-world
applications, such as precision medicine, epidemiology, economy, and marketing.
Many state-of-the-art methods either assume the observations of all confounders
or seek to infer the unobserved ones. We take a different perspective by
assuming unobserved risk factors, i.e., adjustment variables that affect only
the sequence of outcomes. Under unconfoundedness, we target the Individual
Treatment Effect (ITE) estimation with unobserved heterogeneity in the
treatment response due to missing risk factors. We address the challenges posed
by time-varying effects and unobserved adjustment variables. Led by theoretical
results over the validity of the learned adjustment variables and
generalization bounds over the treatment effect, we devise Causal DVAE (CDVAE).
This model combines a Dynamic Variational Autoencoder (DVAE) framework with a
weighting strategy using propensity scores to estimate counterfactual
responses. The CDVAE model allows for accurate estimation of ITE and captures
the underlying heterogeneity in longitudinal data. Evaluations of our model
show superior performance over state-of-the-art models.
