TSGAN: Individual treatment effect estimation for multi-intervention with continuous dosage

Abstract

In recent years, causal inference has achieved great results in recommendation systems, causal chain analysis, and individual treatment effects. The individual treatment effect (ITE), also known as the complier average treatment effect (CATE), is the focus of research in the medical, economic, and political fields. Its purpose is to solve the problem that it is impossible to predict an intervention's impact on individuals when interventions effects vary due to individual differences. Today's research focuses on estimating the counter-fact, that is, predicting the difference in treatment effect between individuals receiving one treatment and receiving another treatment. However, the above study was limited to two interventions and did not consider the issue of therapeutic dose. In this paper, a method combining both the idea of matching that prevalent in traditional ITE estimation, and a generative adversarial neural network (GAN) is proposed to achieve individual effect estimation under multi-intervention with continuous dosage intervention. This paper first proposes the idea of treatment effect space (TES), and proposes a neural network based on GAN, uses an improved discriminator, which takes a different approach from common GAN, using multiple discriminators in parallel structure to achieve discrimination of true samples from treatment space. The model was tested and validated under semi-simulated data.