An improved neural network model for treatment effect estimation

TL;DR

This paper introduces an improved neural network model that leverages covariates and neighboring data to more accurately estimate treatment effects and potential outcomes from observational data.

Contribution

The paper presents a novel neural network architecture that incorporates neighboring instance information to enhance treatment effect estimation accuracy.

Findings

Outperforms state-of-the-art models in treatment effect estimation

Demonstrates improved accuracy in potential outcome prediction

Utilizes covariates and neighboring data for better causal inference

Abstract

Nowadays, in many scientific and industrial fields there is an increasing need for estimating treatment effects and answering causal questions. The key for addressing these problems is the wealth of observational data and the processes for leveraging this data. In this work, we propose a new model for predicting the potential outcomes and the propensity score, which is based on a neural network architecture. The proposed model exploits the covariates as well as the outcomes of neighboring instances in training data. Numerical experiments illustrate that the proposed model reports better treatment effect estimation performance compared to state-of-the-art models.