  Many methods have been proposed to estimate treatment effects with
observational data. Often, the choice of the method considers the application's
characteristics, such as type of treatment and outcome, confounding effect, and
the complexity of the data. These methods implicitly assume that the sample
size is large enough to train such models, especially the neural network-based
estimators. What if this is not the case? In this work, we propose
Causal-Batle, a methodology to estimate treatment effects in small
high-dimensional datasets in the presence of another high-dimensional dataset
in the same feature space. We adopt an approach that brings transfer learning
techniques into causal inference. Our experiments show that such an approach
helps to bring stability to neural network-based methods and improve the
treatment effect estimates in small high-dimensional datasets.
