Presentation Type
Article
Location
Kennesaw, Georgia
Start Date
1-4-2026 12:30 PM
End Date
1-4-2026 1:45 PM
Description
Estimating individualized treatment effects from observational clinical data is a central challenge. Personalized oncology depends on this estimation for meaningful patient-level decisions. Standard variable selection methods tend to over-adjust for spurious covariates in practice. They also under-adjust for true confounders when the causal graph is high-dimensional. This failure arises because optimal adjustment set identification is NP-hard in the general case. This paper proposes QCausalMed, a hybrid quantum-classical pipeline addressing this problem. The system formulates confounder selection as a Quadratic Unconstrained Binary Optimization problem. This QUBO problem is solved using the Quantum Approximate Optimization Algorithm, or QAOA. The resulting quantum-selected minimal adjustment set then feeds into a TARNet neural outcome model. TARNet estimates individualized treatment effects from the causally validated covariate subset. We evaluate the full pipeline on TCGA Breast Invasive Carcinoma data covering 876 patients. The dataset includes 14 clinical and genomic covariates across 2 treatment arms. QCausalMed achieves a normalized root mean square error of 0.38 on the PEHE metric. This represents a 19.1% improvement over LASSO-adjusted TARNet on the same data. It also represents a 26.9% improvement over CausalForest under identical evaluation conditions. The QAOA circuit uses 5 qubits and is simulated via PennyLane throughout all experiments. The quantum-selected adjustment set contains only 6 variables versus 11 for LASSO selection. This reduction lowers over-adjustment bias while preserving full backdoor criterion validity. These results indicate that quantum combinatorial optimization can meaningfully improve causal identification. Classical variable selection methods are outperformed on this clinical genomics task.
QCausalMed: Hybrid Quantum-AI Approaches for Optimizing Causal Inference in Personalized Oncology
Kennesaw, Georgia
Estimating individualized treatment effects from observational clinical data is a central challenge. Personalized oncology depends on this estimation for meaningful patient-level decisions. Standard variable selection methods tend to over-adjust for spurious covariates in practice. They also under-adjust for true confounders when the causal graph is high-dimensional. This failure arises because optimal adjustment set identification is NP-hard in the general case. This paper proposes QCausalMed, a hybrid quantum-classical pipeline addressing this problem. The system formulates confounder selection as a Quadratic Unconstrained Binary Optimization problem. This QUBO problem is solved using the Quantum Approximate Optimization Algorithm, or QAOA. The resulting quantum-selected minimal adjustment set then feeds into a TARNet neural outcome model. TARNet estimates individualized treatment effects from the causally validated covariate subset. We evaluate the full pipeline on TCGA Breast Invasive Carcinoma data covering 876 patients. The dataset includes 14 clinical and genomic covariates across 2 treatment arms. QCausalMed achieves a normalized root mean square error of 0.38 on the PEHE metric. This represents a 19.1% improvement over LASSO-adjusted TARNet on the same data. It also represents a 26.9% improvement over CausalForest under identical evaluation conditions. The QAOA circuit uses 5 qubits and is simulated via PennyLane throughout all experiments. The quantum-selected adjustment set contains only 6 variables versus 11 for LASSO selection. This reduction lowers over-adjustment bias while preserving full backdoor criterion validity. These results indicate that quantum combinatorial optimization can meaningfully improve causal identification. Classical variable selection methods are outperformed on this clinical genomics task.
