Individual Treatment Effect Estimation Through Controlled Neural Network Training in Two Stages. (arXiv:2201.08559v1 [cs.LG])

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 …

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