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 regularization path of balancing weights 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：摘要：本文介绍balnet，一个通过逻辑协变量平衡损失函数实现协变量平衡倾向性得分可扩展路径估计的R包。正则化路径通过Yang与Hastie（2024）的通用弹性网络求解器计算，该求解器支持非光滑惩罚的凸损失函数，以及群组惩罚和特征特定惩罚因子。对于套索惩罚，balnet从最大观测协变量不平衡到用户指定的该最大值比例之间，计算平衡权重的正则化路径。我们通过一个空间像素级平衡应用实例展示该方法——利用卫星野火数据构建处理组平均处理效应的合成控制权重。