aceForest kernel balancing that builds kernels from random forest and BART leaf co-occurrence improves computational and statistical performance for balancing covariates in observational causal inference by prioritizing outcome-relevant nonlinearities and interactions.
Researchers proposed forest kernel balancing as a way to choose which features to balance in observational causal inference. The approach uses kernels implicitly estimated by random forests and Bayesian additive regression trees from co-occurrence in the same terminal leaf node, then balances a summary of that kernel to indirectly learn important nonlinearities and interactions.
- Impact 30%
- 49
- Evidence 25%
- 95
- Scale 20%
- 35
- Confidence 15%
- 87
- Recency 10%
- 92
Updated Sep 1, 2026 · TRV-2026-0950
68Index score