BACKGROUND: Glucagon-like Peptide 1 Receptor Agonists (GLP1RA) may reduce asthma exacerbation (AE) risk, but it is unclear which populations benefit most. Recent pharmacoepidemiologic studies have employed iterative causal forest (iCF), a machine learning (ML) algorithm to identify subgroups with heterogeneous treatment effects. While iCF do not rely on prior knowledge of treatment-variable interactions, it may be constrained by missing or misdefined variables in pharmacepidemiology studies. METHODS: We applied the high-dimensional iterative causal forest (hdiCF), a causal ML algorithm that does not reply on predefined variables, to MarketScan 2016-2020 claims data to identify populations with asthma that might benefit most from GLP1RA in reducing AE risk. We built a GLP1RA vs sulfonylurea new-user cohort with ≥ 1 inpatient or 2 outpatient asthma encounters, excluding patients with non-asthma indications for systemic steroids. Using 599 high-dimensional features from inpatient/outpatient services and pharmacy claims, patients were followed for 6 months from their second prescription. The outcome was acute AE (hospital admission or emergency department visit for asthma). RESULTS: In the overall population, GLP1RA decreased AE risk relative to sulfonylurea: aRD -1.4% (-2.0%, -0.8%). hdiCF identified 3 subgroups based on systemic steroid prescription fills (0, 1, and ≥2): patients with ≥2 systemic steroid prescriptions (GLP1RA: 34 events/1367 individuals; sulfonylurea: 53/1013) benefited most from GLP1RA: aRD -3.8% (-5.3%, -2.2%). CONCLUSIONS: This study demonstrates how automated feature identification can pinpoint clinically relevant subgroups with varying treatment effects. Systemic steroid use, as a proxy for severe asthma, may guide personalized predictions of GLP1RA’s short-term benefits on acute AE.