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A method for estimating the conditional average treatment effect undercondition of censored time-to-event data called BENK (the Beran Estimator withNeural Kernels) is proposed. The main idea behind the method is to apply theBeran estimator for estimating the survival functions of controls andtreatments. Instead of typical kernel functions in the Beran estimator, it isproposed to implement kernels in the form of neural networks of a specific formcalled the neural kernels. The conditional average treatment effect isestimated by using the survival functions as outcomes of the control andtreatment neural networks which consists of a set of neural kernels with sharedparameters. The neural kernels are more flexible and can accurately model acomplex location structure of feature vectors. Various numerical simulationexperiments illustrate BENK and compare it with the well-known T-learner,S-learner and X-learner for several types of the control and treatment outcomefunctions based on the Cox models, the random survival forest and theNadaraya-Watson regression with Gaussian kernels. The code of proposedalgorithms implementing BENK is available in https://github.com/Stasychbr/BENK.

BENK: The Beran Estimator with Neural Kernels for Estimating the Heterogeneous Treatment Effect