The propensity score is widely used for causal inference in observational studies, but common parametric estimators can produce biased and inefficient effect estimates when model assumptions are violated. Nonparametric approaches reduce sensitivity to misspecification but often yield unstable weights and inadequate covariate balance. We propose Local Balance with Calibration, implemented by Neural Networks, a weighting method that combines flexible function approximation with the explicit enforcement of covariate balance and calibration. When used with inverse probability weighting, the proposed estimator produces more stable weights, improved covariate balance, and reduced bias in average treatment effect estimation compared with existing approaches. We further develop an influence-function-based variance estimator that provides accurate uncertainty quantification for the resulting weighted estimators. Numerical studies demonstrate improved efficiency and reliable variance estimation across a range of data-generating scenarios. The method is implemented using the publicly available R package LBCNet.

翻译：倾向得分广泛用于观察性研究中的因果推断，但常见参数估计量在违背模型假设时可能产生有偏和低效的效果估计。非参数方法降低了对错误设定的敏感性，但通常导致不稳定的权重和不足的协变量平衡。我们提出通过神经网络实现的局部平衡校准（Local Balance with Calibration）方法——一种结合灵活函数逼近与明确施加协变量平衡及校准的加权策略。在逆概率加权中使用时，与现有方法相比，该估计量在平均处理效应估计中能产生更稳定的权重、改善协变量平衡并降低偏差。我们进一步开发了基于影响函数的方差估计量，为所得加权估计量提供精准的不确定性量化。数值研究在一系列数据生成场景下展示了改进的效率与可靠的方差估计。该方法通过公开可用的R包LBCNet实现。