  Estimating causal effects from observational data (at either an individual --
or a population -- level) is critical for making many types of decisions. One
approach to address this task is to learn decomposed representations of the
underlying factors of data; this becomes significantly more challenging when
there are confounding factors (which influence both the cause and the effect).
In this paper, we take a generative approach that builds on the recent advances
in Variational Auto-Encoders to simultaneously learn those underlying factors
as well as the causal effects. We propose a progressive sequence of models,
where each improves over the previous one, culminating in the Hybrid model. Our
empirical results demonstrate that the performance of all three proposed models
are superior to both state-of-the-art discriminative as well as other
generative approaches in the literature.
