  A new meta-algorithm for estimating the conditional average treatment effects
is proposed in the paper. The main idea underlying the algorithm is to consider
a new dataset consisting of feature vectors produced by means of concatenation
of examples from control and treatment groups, which are close to each other.
Outcomes of new data are defined as the difference between outcomes of the
corresponding examples comprising new feature vectors. The second idea is based
on the assumption that the number of controls is rather large and the control
outcome function is precisely determined. This assumption allows us to augment
treatments by generating feature vectors which are closed to available
treatments. The outcome regression function constructed on the augmented set of
concatenated feature vectors can be viewed as an estimator of the conditional
average treatment effects. A simple modification of the Co-learner based on the
random subspace method or the feature bagging is also proposed. Various
numerical simulation experiments illustrate the proposed algorithm and show its
outperformance in comparison with the well-known T-learner and X-learner for
several types of the control and treatment outcome functions.
