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

TL;DR

BENK introduces a novel neural kernel-based extension of the Beran estimator for more flexible and accurate estimation of heterogeneous treatment effects in censored survival data, outperforming traditional methods.

Contribution

The paper proposes integrating neural kernels into the Beran estimator to enhance modeling flexibility for treatment effect estimation with censored data.

Findings

BENK outperforms T-learner, S-learner, and X-learner in simulations.

Neural kernels improve the modeling of complex feature structures.

The method is validated with various survival models.

Abstract

A method for estimating the conditional average treatment effect under condition of censored time-to-event data called BENK (the Beran Estimator with Neural Kernels) is proposed. The main idea behind the method is to apply the Beran estimator for estimating the survival functions of controls and treatments. Instead of typical kernel functions in the Beran estimator, it is proposed to implement kernels in the form of neural networks of a specific form called the neural kernels. The conditional average treatment effect is estimated by using the survival functions as outcomes of the control and treatment neural networks which consists of a set of neural kernels with shared parameters. The neural kernels are more flexible and can accurately model a complex location structure of feature vectors. Various numerical simulation experiments illustrate BENK and compare it with the well-known T-learner, S-learner and X-learner for several types of the control and treatment outcome functions based on the Cox models, the random survival forest and the Nadaraya-Watson regression with Gaussian kernels. The code of proposed algorithms implementing BENK is available in https://github.com/Stasychbr/BENK.