Optimizing medical costs and resource utilization using causal inference and explaining the model’s predictions

Abstract

Machine learning (ML) models that accurately predict treatment effects and related healthcare costs can bring significant efficiencies in the healthcare industry. These models could help reduce fatalities resulting from incorrect treatment allocations and contribute to cost-effective healthcare delivery, which is crucial for both developed and developing nations. However, existing literature does not provide any comprehensive framework that effectively estimates both treatment effect and the overall medical expenditure while considering individual treatment effects. To address this gap, we propose CFMedNet, a pioneering counterfactual inference framework that jointly estimates treatment effect and medical costs. This novel framework not only predicts the potential impact and costs of a given treatment and its counterfactual but also provides individual treatment effects for both outcomes. However, a considerable challenge in the adoption of such ML models in healthcare is their perceived ’black box’ nature due to limited transparency in decision-making processes. Since medical professionals bear the responsibility for their decisions, it’s crucial to have Explainable AI (XAI) models, especially in sensitive domains like healthcare. As an innovative contribution, we introduce a post-hoc explainer, GMM-LIME, specifically designed for multi-output causal inference based counterfactual neural networks. This explainer offers crucial explanations and interpretations of our proposed model, thereby improving its transparency and applicability. This dual contribution of a comprehensive estimation framework and in-depth explanatory tools, holds great potential to significantly progress personalized healthcare, balancing economic efficiency with treatment efficacy. Our work represents an integration of Causal Inference, Deep Learning, and XAI, with results obtained from a semi-synthetic dataset.