aceForest Kernel Balancing Weights: Outcome-Guided Features for Causal Inference
While balancing covariates between groups is central for observational causal inference, selecting which features to balance remains a challenging problem. Kernel balancing is a promising approach that first estimates a kernel that captures similarity across units and then balances a (possibly low-dimensional) summary of that kernel, indirectly learning important features to balance. In this paper, we propose forest kernel balancing, which leverages the underappreciated fact that tree-based machine learning mode…
Forest 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.
Evidence
- Peer-reviewedStatistics in Medicine2026-09-01
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Truvace Impact Record TRV-2026-0950, v1: “Forest Kernel Balancing Weights: Outcome-Guided Features for Causal Inference.” Truvace, 2026-09-01. /record/TRV-2026-0950 (accessed at citation time). sha256 0d4229428f8f0231…
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