Estimation of Personalized Heterogeneous Treatment Effects Using Concatenation and Augmentation of Feature Vectors

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. Keywords: treatment effect, meta-learner, regression, treatment, control, simulation 1 Introduction One the most important problems in medicine is to choose the most appropriate treatment for a certain patient which may differ from other patients in her/his clinical or other characteristics [25]. With the increase of the amount of data and with the developing the electronic health record concept in medicine, there is a growing interest to apply machine learning methods to solve the problem of the most appropriate treatment by estimating treatment effects directly from observational data. The main peculiarity of observational data is that it contains past actions, their outcomes, but without direct access to the mechanism which gave rise to the action. Shalit at al. [34] give a clear example of observational data, when we have patient characteristics, medications (action), and outcomes, 1 arXiv:1909.03894v1