B1339
Title: Heterogeneous treatment effect estimation based on a partially linear model with a Gaussian process prior
Authors: Shunsuke Horii - Waseda University (Japan) [presenting]
Abstract: Recently, heterogeneous treatment effect estimation has been attracting a lot of attention due to its importance in various fields. We propose a partially linear model with a Gaussian process prior for the heterogeneous treatment effect estimation. A partially linear model is a semiparametric model that consists of linear and nonparametric components in an additive form. A model that uses a Gaussian process to model the nonparametric component has also been studied in the literature. However, these models cannot handle the heterogeneity of the treatment effect. In the proposed model, not only the nonparametric component of the model, but also the heterogeneous treatment effect of the treatment variable is modeled by a Gaussian process prior. We show the effectiveness of the proposed method through numerical experiments based on synthetic and real-world data.