Application of Propensity Score Models and Causal Estimators in Observational Studies under Model Misspecification
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模型误设下观测研究中倾向得分模型与因果估计量的应用
- 研究问题
- 倾向得分和结局模型不同程度误设时，响应面、逆概率加权及增广逆概率加权如何表现，机器学习倾向得分又会带来哪些影响？
- 方法与思路
- 作者比较逻辑回归、随机森林、支持向量机及线性判别倾向得分，在不同模型设定、样本量和协变量相关结构下模拟并进行数据应用。
- 材料陈述的贡献
- 摘要报告，多数模拟场景下增广逆概率加权较稳健，逆概率加权对倾向得分误设较敏感，响应面表现依赖结局模型正确设定。
- 阅读关注点
- 应核对误设场景的构造、重叠与极端权重处理、机器学习调参及推断程序，并检查区间宽度评价是否同时考虑覆盖率。
- 阅读边界
- 这是 arXiv 预印本，同行评审与期刊接收未核实；摘要未提供完整模拟数值，双重稳健性不保证两个模型均误设时仍然有效。
原文摘要
Propensity score (PS) methods are widely used in observational studies to reduce confounding and estimate causal treatment effects. However, the validity of PS-based causal estimators depends heavily on correct model specification, and model misspecification may lead to substantial bias and instability. In this study, we systematically evaluate the performance of commonly used causal estimators, including response surface modeling (RSM), inverse probability weighting (IPW), and augmented inverse probability weighting (AIPW), under varying levels of PS and outcome model misspecification. We compare classical logistic regression with several machine learning approaches for PS estimation, including random forests (RF), support vector machines (SVM), and linear discriminant analysis (LDA). Extensive simulation studies were conducted under multiple scenarios defined by combinations of correctly specified and misspecified PS and outcome models, varying sample sizes, and different covariate correlation structures. Estimator performance was assessed using bias, absolute bias, root mean squared error, empirical standard error, and confidence interval width. Results demonstrate that AIPW consistently provides robust and stable estimates across most scenarios due to its doubly robust property, whereas IPW is highly sensitive to PS misspecification and unstable PS estimates produced by flexible machine learning methods. RSM performs well only when the outcome model is correctly specified. Real-world applications using the ACTG175 clinical trial and the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset further illustrate the practical implications of estimator choice and PS modeling strategy. Overall, our findings highlight the importance of integrating flexible machine learning approaches within doubly robust frameworks to improve causal effect estimation in observational studies.