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Estimating Heterogeneous Causal Effects with Tree-Based Methods under Imperfect Compliance — arXiv:2609.31769

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.31769
License (as archived): CC0-1.0
Archived: 2026-10-07T23:03:22.744Z
SHA-256 of the archived text: 126d78c532cbd3be18ae271516755a87f65d0bd412b41c6403bb0d8d6fb096c3
Stable URL of this page: https://metraipolis.com/documento/dc_7dada8a4f53d

How to cite

Estimating Heterogeneous Causal Effects with Tree-Based Methods under Imperfect Compliance — arXiv:2609.31769 — arXiv (en), https://arxiv.org/abs/2609.31769 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_7dada8a4f53d (Metraipolis archive dc_7dada8a4f53d, sha256 126d78c532cbd3be18ae271516755a87f65d0bd412b41c6403bb0d8d6fb096c3)

BibTeX

@misc{dc7dada8a4f53d,
  title = {Estimating Heterogeneous Causal Effects with Tree-Based Methods under Imperfect Compliance — arXiv:2609.31769},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.31769},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_7dada8a4f53d; sha256 126d78c532cbd3be18ae271516755a87f65d0bd412b41c6403bb0d8d6fb096c3},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.31769. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

We study the impact of conditional complier average causal effect (CCACE) estimation methods on the performance of subgroup discovery and heterogeneous causal effect estimation under imperfect compliance. Building on the Bayesian Causal Forest with Instrumental Variable (BCF-IV) (Bargagli-Stoffi et al. (2022)) method, we introduce a two-step, model-agnostic approach that allows any suitable machine learning method to be used for the CCACE estimation in the first step. Specifically, we implement two non-Bayesian tree-based methods, both using forest-based learners: DRRF-IV, a debiased transformed-outcome regression-forest approach, and a GRF-based IV adaptation of the generalized random forest framework (Athey et al., 2019). Through a simulation study, we assess the precision, bias, and the ability to correctly identify the underlying subgroup structure of the proposed methods relative to BCF-IV. The results show that the non-Bayesian methods perform competitively across the considered simulation settings, with performance improving for larger sample sizes and moderately large treatment effects, while reducing computational runtime. We apply our new methods by revisiting an empirical study that examines the effect of prompt admission to intensive care units (ICU) on 28-day mortality across 48 UK National Health Service hospitals. While previous work finds no significant overall treatment effect, we investigate whether subgroups of patients may benefit more from prompt ICU admission.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-29 in stat.ME).
Categorie arXiv: Methodology (stat.ME) ; Econometrics (econ.EM).
Pagina del lavoro: https://arxiv.org/abs/2609.31769 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-07T22:41:46.564Z dalla pagina https://arxiv.org/catchup/stat.ME/2026-09-29?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).