  Nowadays, in many scientific and industrial fields there is an increasing
need for estimating treatment effects and answering causal questions. The key
for addressing these problems is the wealth of observational data and the
processes for leveraging this data. In this work, we propose a new model for
predicting the potential outcomes and the propensity score, which is based on a
neural network architecture. The proposed model exploits the covariates as well
as the outcomes of neighboring instances in training data. Numerical
experiments illustrate that the proposed model reports better treatment effect
estimation performance compared to state-of-the-art models.
