  Causal effect estimation aims at estimating the Average Treatment Effect as
well as the Conditional Average Treatment Effect of a treatment to an outcome
from the available data. This knowledge is important in many safety-critical
domains, where it often needs to be extracted from observational data. In this
work, we propose a new causal inference model, named C-XGBoost, for the
prediction of potential outcomes. The motivation of our approach is to exploit
the superiority of tree-based models for handling tabular data together with
the notable property of causal inference neural network-based models to learn
representations that are useful for estimating the outcome for both the
treatment and non-treatment cases. The proposed model also inherits the
considerable advantages of XGBoost model such as efficiently handling features
with missing values requiring minimum preprocessing effort, as well as it is
equipped with regularization techniques to avoid overfitting/bias. Furthermore,
we propose a new loss function for efficiently training the proposed causal
inference model. The experimental analysis, which is based on the performance
profiles of Dolan and Mor{\'e} as well as on post-hoc and non-parametric
statistical tests, provide strong evidence about the effectiveness of the
proposed approach.
