We present balnet, an R package for scalable pathwise estimation of covariate balancing propensity scores via logistic covariate balancing loss functions. Regularization paths are computed with Yang and Hastie (2024)'s generic elastic net solver, supporting convex losses with non-smooth penalties, as well as group penalties and feature-specific penalty factors. For lasso penalization, balnet computes a regularized balance path from the largest observed covariate imbalance to a user-specified fraction of this maximum. We illustrate the method with an application to spatial pixel-level balancing for constructing synthetic control weights for the average treatment effect on the treated, using satellite data on wildfires.

翻译：本文介绍balnet，一个用于通过逻辑协变量平衡损失函数进行可扩展路径估计协变量平衡倾向得分的R软件包。正则化路径采用Yang和Hastie（2024）提出的通用弹性网络求解器进行计算，支持具有非光滑惩罚项的凸损失函数，以及组惩罚和特征特定惩罚因子。对于lasso惩罚，balnet计算从观测到的最大协变量不平衡到用户指定的该最大值比例的规则化平衡路径。我们通过卫星野火数据在空间像素级平衡中的应用示例，展示了该方法在构建处理组平均处理效应的合成控制权重方面的实际应用。