  In recent years, there has been a growing interest in using machine learning
techniques for the estimation of treatment effects. Most of the best-performing
methods rely on representation learning strategies that encourage shared
behavior among potential outcomes to increase the precision of treatment effect
estimates. In this paper we discuss and classify these models in terms of their
algorithmic inductive biases and present a new model, NN-CGC, that considers
additional information from the causal graph. NN-CGC tackles bias resulting
from spurious variable interactions by implementing novel constraints on
models, and it can be integrated with other representation learning methods. We
test the effectiveness of our method using three different base models on
common benchmarks. Our results indicate that our model constraints lead to
significant improvements, achieving new state-of-the-art results in treatment
effects estimation. We also show that our method is robust to imperfect causal
graphs and that using partial causal information is preferable to ignoring it.
