{
  "abstract": "Covariate balancing is widely used for estimating causal effects in observational studies. A central challenge is determining which covariate features should be balanced. Forest-kernel balancing provides a data-driven way to learn informative balancing features. Existing forest-kernel balancing methods first learn a tree-based representation and then construct balancing weights. However, this procedure creates a mismatch, as limited tree capacity may be spent on features that are already well represented. We propose Balance-Guided Forest (BGF), which uses the current balancing weights to guide subsequent tree construction. BGF grows trees using a novel splitting score that combines residual imbalance with an outcome contrast, favoring splits that separate outcome-relevant regions where imbalance remains. This couples representation learning with covariate balancing rather than treating balancing as a downstream step. We establish an oracle-type bound that links the iterative construction to weighting error and causal bias. Simulations exhibit improvements in causal estimation relative to existing forest-kernel approaches. Real-data applications illustrate the practical behavior of BGF across observational studies.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.05723",
  "author_total": 2,
  "authors": [
    {
      "name": "Feiyang Yi"
    },
    {
      "name": "Jelena Bradic"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/295yz4np8tnb1.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.05723",
  "fulltext_url": null,
  "id": "295yz4np8tnb1",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.05723",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "stat.ME"
    ],
    [
      "arxiv",
      "stat.ML"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2611.05723",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-03",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/295yz4np8tnb1.ris",
  "sources": [
    {
      "first_seen": "2026-11-05T15:21:06Z",
      "native_id": "2611.05723",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.05723"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ME",
      "subfield": "statistics"
    }
  ],
  "title": "Balance-Guided Forest for Causal Effect Estimation",
  "title_text": "Balance-Guided Forest for Causal Effect Estimation",
  "type": "preprint",
  "updated_at": "2026-11-09T00:20:32Z",
  "url": "https://research.draconitic.com/paper/295yz4np8tnb1",
  "venue": "arXiv",
  "versions": []
}