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A new meta-algorithm for estimating the conditional average treatment effects is pro-posed in the paper. The basic idea behind 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. [ABSTRACT FROM AUTHOR]

Microsurgery is a highly complex surgical procedure on small body parts performed by a dedicated surgical team. An operating microscope is typically used to obtain a precise view of the soft tissues. The complexity of the microsurgical procedure makes it a suitable application of virtual/augmented reality technology for training purpose. In this paper, we present an overview of our simulator and then describe in details the visualization work that reconstructs the magnified view of the operating area from medical images. The visualization component is based entirely on our newly proposed dynamic linear level octree (DLLO)-based volume rendering methods. In comparison with the conventional ray casting algorithms, our computational method achieves fast rendering speed, reduces hardware requirement and combines surface-volume rendering for realistic and interactive PC-based medical simulations. The new algorithms are suitable for rendering dynamic medical data which are increasingly popular clinically. [ABSTRACT FROM AUTHOR]