Self Balancing Neural Network: A Novel Method to Estimate Average Treatment Effect

TL;DR

This paper introduces the Self Balancing Neural Network (Sbnet), a novel deep learning approach that automatically estimates propensity scores to accurately determine average treatment effects in observational studies, outperforming existing methods.

Contribution

The study proposes Sbnet, a neural network that self-estimates propensity scores within its architecture, eliminating the need for separate propensity score modeling and reducing bias in treatment effect estimation.

Findings

Sbnet outperforms state-of-the-art methods in simulations.

The method effectively reduces bias in observational data analysis.

Sbnet demonstrates superior accuracy on real-world datasets.

Abstract

In observational studies, confounding variables affect both treatment and outcome. Moreover, instrumental variables also influence the treatment assignment mechanism. This situation sets the study apart from a standard randomized controlled trial, where the treatment assignment is random. Due to this situation, the estimated average treatment effect becomes biased. To address this issue, a standard approach is to incorporate the estimated propensity score when estimating the average treatment effect. However, these methods incur the risk of misspecification in propensity score models. To solve this issue, a novel method called the "Self balancing neural network" (Sbnet), which lets the model itself obtain its pseudo propensity score from the balancing net, is proposed in this study. The proposed method estimates the average treatment effect by using the balancing net as a key part of the feedforward neural network. This formulation resolves the estimation of the average treatment effect in one step. Moreover, the multi-pseudo propensity score framework, which is estimated from the diversified balancing net and used for the estimation of the average treatment effect, is presented. Finally, the proposed methods are compared with state-of-the-art methods on three simulation setups and real-world datasets. It has been shown that the proposed self-balancing neural network shows better performance than state-of-the-art methods.