  Much research has been devoted to the problem of estimating treatment effects
from observational data; however, most methods assume that the observed
variables only contain confounders, i.e., variables that affect both the
treatment and the outcome. Unfortunately, this assumption is frequently
violated in real-world applications, since some variables only affect the
treatment but not the outcome, and vice versa. Moreover, in many cases only the
proxy variables of the underlying confounding factors can be observed. In this
work, we first show the importance of differentiating confounding factors from
instrumental and risk factors for both average and conditional average
treatment effect estimation, and then we propose a variational inference
approach to simultaneously infer latent factors from the observed variables,
disentangle the factors into three disjoint sets corresponding to the
instrumental, confounding, and risk factors, and use the disentangled factors
for treatment effect estimation. Experimental results demonstrate the
effectiveness of the proposed method on a wide range of synthetic, benchmark,
and real-world datasets.
