  The scarcity of high-dimensional causal inference datasets restricts the
exploration of complex deep models. In this work, we propose a method to
generate a synthetic causal dataset that is high-dimensional. The synthetic
data simulates a causal effect using the MNIST dataset with Bernoulli treatment
values. This provides an opportunity to study varieties of models for causal
effect estimation. We experiment on this dataset using Dragonnet architecture
(Shi et al. (2019)) and modified architectures. We use the modified
architectures to explore different types of initial Neural Network layers and
observe that the modified architectures perform better in estimations. We
observe that residual and transformer models estimate treatment effect very
closely without the need for targeted regularization, introduced by Shi et al.
(2019).
