  Individual Treatment Effect (ITE) prediction is an important area of research
in machine learning which aims at explaining and estimating the causal impact
of an action at the granular level. It represents a problem of growing interest
in multiple sectors of application such as healthcare, online advertising or
socioeconomics. To foster research on this topic we release a publicly
available collection of 13.9 million samples collected from several randomized
control trials, scaling up previously available datasets by a healthy 210x
factor. We provide details on the data collection and perform sanity checks to
validate the use of this data for causal inference tasks. First, we formalize
the task of uplift modeling (UM) that can be performed with this data, along
with the relevant evaluation metrics. Then, we propose synthetic response
surfaces and heterogeneous treatment assignment providing a general set-up for
ITE prediction. Finally, we report experiments to validate key characteristics
of the dataset leveraging its size to evaluate and compare - with high
statistical significance - a selection of baseline UM and ITE prediction
methods.
