Bayesian Network Propensity Score to Evaluate Treatment Effects in Observational Studies
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用于观察性研究处理效应评估的贝叶斯网络倾向得分
- 研究问题
- 观察性研究的协变量具有复杂依赖、处理组可能不平衡时，如何估计倾向得分并获得可靠的平均处理效应推断？
- 方法与思路
- 作者以贝叶斯网络建模协变量的条件依赖，再结合Hájek估计量估计平均处理效应，并开展模拟和临床数据应用。
- 材料陈述的贡献
- 摘要称方法具有一致性、渐近正态性及效率性质，并在十五种模拟情境的拒绝率与覆盖准确性上优于所列基准。
- 阅读关注点
- 应核对网络结构如何学习、倾向得分与效应估计的条件、方差估计，以及模拟中的基准实现和检验设定。
- 阅读边界
- 这是arXiv预印本，同行评审和期刊接收未获确认；摘要未说明混杂控制条件，模拟比较也不能证明普遍优越性。
原文摘要
This paper focuses on the Bayesian Network Propensity Score (BNPS), a novel approach for estimating treatment effects in observational studies characterized by unknown (and likely unbalanced) designs and complex dependency structures among covariates. Traditional methods, such as logistic regression, often impose rigid parametric assumptions that may lead to misspecification errors, compromising causal inference. Recent classical and machine learning alternatives, such as boosted CART, random forests, and Stable Balancing Weights, seem to be attractive in a predictive perspective, but they typically lack asymptotic properties, such as consistency, efficiency, and valid variance estimation. In contrast, the recently proposed BNPS to estimate propensity scores uses Bayesian Networks to flexibly model conditional dependencies while preserving essential statistical properties such as consistency, asymptotic normality and asymptotic efficiency. Combined with the Hájek estimator, BNPS enables robust estimation of the Average Treatment Effect (ATE) in scenarios with strong covariate interactions and unknown data-generating mechanisms. Through extensive simulations across fifteen realistic scenarios and varying sample sizes, BNPS consistently outperforms benchmark methods in both empirical rejection rates and coverage accuracy. Finally, an application to a real-world dataset of 7,162 prostate cancer patients from San Raffaele Hospital (Milan, Italy) demonstrates BNPS's practical value in assessing the impact of pelvic lymph node dissection on hospitalization duration and biochemical recurrence. The findings support BNPS as a statistically robust, interpretable and transparent alternative for causal inference in complex observational settings, enhancing the reliability of evidence from real-world biomedical data.