GeoACE: Geometry-Diverse Anchor-Correction Ensembles for Heterogeneous Treatment Effect Estimation
Predicting heterogeneous causal effects supports informed decision-making, yet suitable inductive biases vary with treatment overlap, covariate imbalance, prognostic structure, and sample size. The Geometry-Diverse Anchor--Correction Expert Ensemble (GeoACE) addresses this variation through five complementary experts whose predictions are combined using weights estimated from validation data. Compared with ensemble designs that derive diversity primarily from sample or feature subsets, reweighting, or feature transformations, its central contribution is the coordinated design of training objectives and anchor geometries that target different prognostic and overlap structures. Following validation, each expert is refitted on the combined training and validation data, while retaining its distinctive structure. The shared architecture couples a structured outcome anchor with a learned neural correction. ACE serves as the reference estimator, while OW-ACE emphasizes well-supported covariate regions through overlap-weighted factual training. G$Φ$-ACE constructs a global outcome-guided geometry from pooled prognostic associations, whereas A$Φ$-ACE incorporates arm-specific outcome associations and penalties for treatment-group imbalance. O$Φ$-ACE complements these approaches with an outcome-free, overlap-aware projection that replaces the anchor input with a lower-dimensional representation. GeoACE is evaluated against 11 comparators on eight benchmark protocols, alongside comparisons of weighting rules and aggregation controls. It achieves the lowest mean $\sqrt{\mathrm{PEHE}}$ and absolute average treatment effect error on all three IHDP protocols and closely approaches the best comparator on NEWS, although performance varies across the remaining benchmarks. The aggregation analyses show similar observed performance for inverse-DR weighting and equal averaging.