ArticleAmerican journal of epidemiology2024
Iterative Causal Forest: A Novel Algorithm for Subgroup Identification.
Article in American journal of epidemiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Heterogeneous effects of preoperative beta blockers on 30-day mortality after coronary artery bypass surgery.Annals of medicine · 2026Article
- Multifactorial attribution for vancomycin-associated acute kidney injury.British journal of clinical pharmacology · 2026Article
- Circadian syndrome (CircS) and cognitive trajectory deterioration in middle-aged and older adults: A national cohort study with causal forest analysis.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- The heterogeneous treatment effects of statins on dementia: a target trial emulation with causal machine learning using integrated genetic and real-world data.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Investigation of risk factors for osteoporosis with a focus on hypertension and estimation of the causal effect of hypertension on osteoporosis using causal forest.Hypertension research : official journal of the Japanese Society of Hypertension · 2025Article
- A novel high dimensional approach to assess heterogeneous treatment effect in claims data.American journal of epidemiology · 2025Article
- Glucagon-like Peptide-1 Receptor Agonists in Asthma Exacerbations: An Application of High-Dimensional Iterative Causal Forest to Identify Subgroups.Pharmacoepidemiology and drug safety · 2025Article
- An overview of modern machine learning methods for effect measure modification analyses in high-dimensional settings.SSM - population health · 2025Article
- Two-step pragmatic subgroup discovery for heterogeneous treatment effects analyses: perspectives toward enhanced interpretability.European journal of epidemiology · 2025Article
- Prognosis modelling of adverse events for post-PCI treated AMI patients based on inflammation and nutrition indexes.BMC cardiovascular disorders · 2025Article
- Glucagon-like Peptide 1 Receptor Agonists and Asthma Exacerbations: Which Patients Benefit Most?Annals of the American Thoracic Society · 2024Article
- High-dimensional Iterative Causal Forest (hdiCF) for Subgroup Identification Using Health Care Claims Data.American journal of epidemiology · 2024Article
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Authors and funding
9 authors.
Funding
Abstract
Precisely and efficiently identifying subgroups with heterogeneous treatment effects (HTEs) in real-world evidence studies remains a challenge. Based on the causal forest (CF) method, we developed an iterative CF (iCF) algorithm to identify HTEs in subgroups defined by important variables. Our method iteratively grows different depths of the CF with important effect modifiers, performs plurality votes to obtain decision trees (subgroup decisions) for a family of CFs with different depths, and then finds the cross-validated subgroup decision that best predicts the treatment effect as a final subgroup decision. We simulated 12 different scenarios and showed that the iCF outperformed other machine learning methods for interaction/subgroup identification in the majority of scenarios assessed. Using a 20% random sample of fee-for-service Medicare beneficiaries initiating sodium-glucose cotransporter-2 inhibitors or glucagon-like peptide-1 receptor agonists, we implemented the iCF to identify subgroups with HTEs for hospitalized heart failure. Consistent with previous studies suggesting patients with heart failure benefit more from sodium-glucose cotransporter-2 inhibitors, iCF successfully identified such a subpopulation with HTEs and additive interactions. The iCF is a promising method for identifying subgroups with HTEs in real-world data where the potential for unmeasured confounding can be limited by study design.
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