  We develop a Causal-Deep Neural Network (CDNN) model trained in two stages to
infer causal impact estimates at an individual unit level. Using only the
pre-treatment features in stage 1 in the absence of any treatment information,
we learn an encoding for the covariates that best represents the outcome. In
the $2^{nd}$ stage we further seek to predict the unexplained outcome from
stage 1, by introducing the treatment indicator variables alongside the encoded
covariates. We prove that even without explicitly computing the treatment
residual, our method still satisfies the desirable local Neyman orthogonality,
making it robust to small perturbations in the nuisance parameters.
Furthermore, by establishing connections with the representation learning
approaches, we create a framework from which multiple variants of our algorithm
can be derived. We perform initial experiments on the publicly available data
sets to compare these variants and get guidance in selecting the best variant
of our CDNN method. On evaluating CDNN against the state-of-the-art approaches
on three benchmarking datasets, we observe that CDNN is highly competitive and
often yields the most accurate individual treatment effect estimates. We
highlight the strong merits of CDNN in terms of its extensibility to multiple
use cases.
