balnet: Pathwise Estimation of Covariate Balancing Propensity Scores

TL;DR

Balnet is an R package that efficiently estimates covariate balancing propensity scores using scalable regularization paths, supporting various penalties and applied to wildfire satellite data.

Contribution

It introduces a new scalable method for covariate balancing propensity score estimation with flexible regularization options and demonstrates its application to spatial data balancing.

Findings

Supports convex losses with non-smooth penalties.

Computes regularization paths for balancing weights.

Applied to wildfire satellite data for treatment effect estimation.

Abstract

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.