Package: forestBalance 0.1.1
forestBalance: Balancing Confounder Distributions with Forest Energy Balancing
Estimates average treatment effects using kernel energy balancing with random forest similarity kernels. A multivariate random forest jointly models covariates, outcome, and treatment to build a similarity kernel between observations. This kernel is then used for energy balancing to create weights that control for confounding. The method is described in De and Huling (2025) <doi:10.48550/arXiv.2512.18069>.
Authors:
forestBalance_0.1.1.tar.gz
forestBalance_0.1.1.zip(r-4.7)forestBalance_0.1.1.zip(r-4.6)forestBalance_0.1.1.zip(r-4.5)
forestBalance_0.1.1.tgz(r-4.6-x86_64)forestBalance_0.1.1.tgz(r-4.6-arm64)forestBalance_0.1.1.tgz(r-4.5-x86_64)forestBalance_0.1.1.tgz(r-4.5-arm64)
forestBalance_0.1.1.tar.gz(r-4.7-arm64)forestBalance_0.1.1.tar.gz(r-4.7-x86_64)forestBalance_0.1.1.tar.gz(r-4.6-arm64)forestBalance_0.1.1.tar.gz(r-4.6-x86_64)
forestBalance_0.1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
forestBalance/json (API)
| # Install 'forestBalance' in R: |
| install.packages('forestBalance', repos = c('https://jaredhuling.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/jaredhuling/forestbalance/issues
Last updated from:057d235b84. Checks:11 WARNING, 2 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | WARNING | 151 | ||
| linux-devel-x86_64 | WARNING | 163 | ||
| source / vignettes | OK | 234 | ||
| linux-release-arm64 | WARNING | 144 | ||
| linux-release-x86_64 | WARNING | 178 | ||
| macos-release-arm64 | WARNING | 128 | ||
| macos-release-x86_64 | WARNING | 179 | ||
| macos-oldrel-arm64 | WARNING | 106 | ||
| macos-oldrel-x86_64 | WARNING | 190 | ||
| windows-devel | WARNING | 132 | ||
| windows-release | WARNING | 186 | ||
| windows-oldrel | WARNING | 151 | ||
| wasm-release | OK | 123 |
Exports:compute_balanceforest_balanceforest_kernelget_leaf_node_matrixkernel_balanceleaf_node_kernelleaf_node_kernel_Zsimulate_data
Dependencies:DiceKriginggrflatticelmtestMASSMatrixRcppRcppEigensandwichzoo
Augmented (Doubly-Robust) Estimation
Rendered fromaugmented.Rmdusingknitr::rmarkdownon May 08 2026.Last update: 2026-03-27
Started: 2026-03-20
Cross-Fitting for Debiased Kernel Estimation
Rendered fromcrossfitting.Rmdusingknitr::rmarkdownon May 08 2026.Last update: 2026-03-27
Started: 2026-03-20
Getting Started with forestBalance
Rendered fromguide.Rmdusingknitr::rmarkdownon May 08 2026.Last update: 2026-03-27
Started: 2026-03-19
Performance and Scalability
Rendered fromperformance.Rmdusingknitr::rmarkdownon May 08 2026.Last update: 2026-03-27
Started: 2026-03-19
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| forestBalance: Forest Kernel Energy Balancing for Causal Inference | forestBalance-package forestBalance |
| Compute covariate balance diagnostics for a set of weights | compute_balance print.forest_balance_diag |
| Estimate ATE using forest-based kernel energy balancing | forest_balance |
| Compute random forest proximity kernel from a GRF forest | forest_kernel |
| Extract leaf node membership matrix from a GRF forest | get_leaf_node_matrix |
| Kernel energy balancing weights via closed-form solution | kernel_balance |
| Compute random forest proximity kernel from a leaf node matrix | leaf_node_kernel |
| Build the sparse indicator matrix Z from a leaf node matrix | leaf_node_kernel_Z |
| Print a forest_balance object | print.forest_balance |
| Simulate observational study data with confounding | simulate_data |
| Summarize a forest_balance object | print.summary.forest_balance summary.forest_balance |
