Background:

The generalized propensity score (GPS) addresses selection bias due to observed confounding variables and provides a means to demonstrate causality of continuous treatment doses with propensity score analyses. Estimating the GPS with parametric models obliges researchers to meet improbable conditions such as correct model specification, normal distribution of variables, and large sample sizes.

Objectives:

The purpose of this Monte Carlo simulation study is to examine the performance of neural networks as compared to full factorial regression models to estimate GPS in the presence of Gaussian and skewed treatment doses and small to moderate sample sizes.

Research design:

A detailed conceptual introduction of neural networks is provided, as well as an illustration of selection of hyperparameters to estimate GPS. An example from public health and nutrition literature uses residential distance as a treatment variable to illustrate how neural networks can be used in a propensity score analysis to estimate a dose–response function of grocery spending behaviors.

Results:

We found substantially higher correlations and lower mean squared error values after comparing true GPS with the scores estimated by neural networks. The implication is that more selection bias was removed using GPS estimated with neural networks than using GPS estimated with classical regression.

Conclusions:

This study proposes a new methodological procedure, neural networks, to estimate GPS. Neural networks are not sensitive to the assumptions of linear regression and other parametric models and have been shown to be a contender against parametric approaches to estimate propensity scores for continuous treatments.

中文翻译：

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神经网络估计连续治疗剂量的广义倾向评分

 背景：

广义倾向评分（GPS）解决了由于观察到的混杂变量而导致的选择偏差，并提供了一种通过倾向评分分析来证明连续治疗剂量的因果关系的方法。使用参数模型估计 GPS 要求研究人员满足不可能的条件，例如正确的模型规范、变量的正态分布和大样本量。

 目标：

这项蒙特卡罗模拟研究的目的是检查神经网络与全阶乘回归模型相比的性能，以在存在高斯和倾斜治疗剂量以及小到中等样本量的情况下估计 GPS。

 研究设计：

提供了神经网络的详细概念介绍，以及用于估计 GPS 的超参数选择的说明。公共卫生和营养文献中的一个例子使用居住距离作为治疗变量来说明如何在倾向评分分析中使用神经网络来估计杂货消费行为的剂量反应函数。

 结果：

将真实 GPS 与神经网络估计的分数进行比较后，我们发现相关性明显更高，均方误差值更低。这意味着，使用神经网络估计的 GPS 比使用经典回归估计的 GPS 消除了更多的选择偏差。

 结论：

这项研究提出了一种新的方法程序——神经网络来估计 GPS。神经网络对线性回归和其他参数模型的假设不敏感，并且已被证明是用于估计连续治疗倾向得分的参数方法的有力竞争者。