forestBalance: Forest Kernel Energy Balancing for Causal Inference
Source:R/forestBalance-package.R
forestBalance-package.RdEstimates average treatment effects (ATE) using kernel energy balancing with random forest similarity kernels. A multivariate random forest jointly models covariates, treatment, and outcome to build a proximity kernel that captures confounding structure. Balancing weights are obtained via a closed-form kernel energy distance solution.
Main function
forest_balance is the primary interface. It fits the forest,
constructs the kernel, computes balancing weights, and estimates the ATE.
By default it uses K-fold cross-fitting and an adaptive leaf size to minimize
overfitting bias.
Key features
Adaptive
min.node.sizethat scales with \(n\) and \(p\)K-fold cross-fitting to reduce kernel overfitting bias
Rcpp-accelerated leaf node extraction
Sparse kernel construction via single
tcrossprodConjugate gradient solver for large \(n\) (avoids forming the kernel matrix entirely)
Lower-level interface
For more control, the pipeline can be run step by step:
get_leaf_node_matrix, leaf_node_kernel,
kernel_balance.
References
De, S. and Huling, J.D. (2025). Data adaptive covariate balancing for causal effect estimation for high dimensional data. arXiv preprint arXiv:2512.18069.