A new method, based on Bayesian Networks, to estimate propensity scores is proposed with the purpose to draw causal inference from real world data on the average treatment effect in case of a binary outcome and discrete covariates. The proposed method ensures maximum likelihood properties to the estimated propensity score, i.e. asymptotic efficiency, thus outperforming other available approach. Two point estimators via inverse probability weighting are then proposed, and their main distributional properties are derived for constructing confidence interval and for testing the hypotheses of absence of the treatment effect. Empirical evidence of the substantial improvements offered by the proposed methodology versus standard logistic modelling of propensity score is provided in simulation settings that mimic the characteristics of a real dataset of prostate cancer patients from Milan San Raffaele Hospital.

翻译：提出了一种基于贝叶斯网络的新方法用于估计倾向性评分，旨在从真实世界数据中对二元结局和离散协变量情形下的平均处理效应进行因果推断。该方法确保所估计的倾向性评分具备最大似然性质（即渐近有效性），因此优于现有其他方法。随后提出了两种基于逆概率加权的点估计量，并推导了其主要分布性质，可用于构建置信区间以及检验处理效应缺失的假设。通过模拟设置（模拟米兰圣拉斐尔医院前列腺癌患者真实数据集的特征）提供了经验证据，证明所提方法相较于标准逻辑回归建模倾向性评分具有显著改进效果。