  Estimation of causal effects is the core objective of many scientific
disciplines. However, it remains a challenging task, especially when the
effects are estimated from observational data. Recently, several promising
machine learning models have been proposed for causal effect estimation. The
evaluation of these models has been based on the mean values of the error of
the Average Treatment Effect (ATE) as well as of the Precision in Estimation of
Heterogeneous Effect (PEHE). In this paper, we propose to complement the
evaluation of causal inference models using concrete statistical evidence,
including the performance profiles of Dolan and Mor{\'e}, as well as
non-parametric and post-hoc statistical tests. The main motivation behind this
approach is the elimination of the influence of a small number of instances or
simulation on the benchmarking process, which in some cases dominate the
results. We use the proposed evaluation methodology to compare several
state-of-the-art causal effect estimation models.
