Robust propensity score estimation via loss function calibration.

IF 1.6 3区 医学 Q3 HEALTH CARE SCIENCES & SERVICES
Statistical Methods in Medical Research Pub Date : 2025-03-01 Epub Date: 2025-02-12 DOI:10.1177/09622802241308709
Yimeng Shang, Yu-Han Chiu, Lan Kong
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引用次数: 0

Abstract

Propensity score estimation is often used as a preliminary step to estimate the average treatment effect with observational data. Nevertheless, misspecification of propensity score models undermines the validity of effect estimates in subsequent analyses. Prediction-based machine learning algorithms are increasingly used to estimate propensity scores to allow for more complex relationships between covariates. However, these approaches may not necessarily achieve covariates balancing. We propose a calibration-based method to better incorporate covariate balance properties in a general modeling framework. Specifically, we calibrate the loss function by adding a covariate imbalance penalty to standard parametric (e.g. logistic regressions) or machine learning models (e.g. neural networks). Our approach may mitigate the impact of model misspecification by explicitly taking into account the covariate balance in the propensity score estimation process. The empirical results show that the proposed method is robust to propensity score model misspecification. The integration of loss function calibration improves the balance of covariates and reduces the root-mean-square error of causal effect estimates. When the propensity score model is misspecified, the neural-network-based model yields the best estimator with less bias and smaller variance as compared to other methods considered.

基于损失函数校准的稳健倾向评分估计。
倾向得分估计常被用作初步步骤,以估计平均治疗效果的观察数据。然而,倾向得分模型的错误说明破坏了后续分析中效果估计的有效性。基于预测的机器学习算法越来越多地用于估计倾向分数,以允许协变量之间更复杂的关系。然而,这些方法不一定能达到协变量平衡。我们提出了一种基于校准的方法,以更好地将协变量平衡属性纳入一般建模框架。具体来说,我们通过向标准参数(例如逻辑回归)或机器学习模型(例如神经网络)添加协变量失衡惩罚来校准损失函数。我们的方法可以通过明确地考虑倾向分数估计过程中的协变量平衡来减轻模型错误规范的影响。实证结果表明,该方法对倾向评分模型的错误描述具有较强的鲁棒性。损失函数校准的积分改善了协变量的平衡,降低了因果效应估计的均方根误差。当倾向评分模型被错误指定时,与其他考虑的方法相比,基于神经网络的模型产生了偏差较小、方差较小的最佳估计器。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Statistical Methods in Medical Research
Statistical Methods in Medical Research 医学-数学与计算生物学
CiteScore
4.10
自引率
4.30%
发文量
127
审稿时长
>12 weeks
期刊介绍: Statistical Methods in Medical Research is a peer reviewed scholarly journal and is the leading vehicle for articles in all the main areas of medical statistics and an essential reference for all medical statisticians. This unique journal is devoted solely to statistics and medicine and aims to keep professionals abreast of the many powerful statistical techniques now available to the medical profession. This journal is a member of the Committee on Publication Ethics (COPE)
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