Exploring the Future of Health with Causal AI: Our Research Themes
Welcome to the core of our research! At the Graphical Models in AI: Discovery and Application cluster, we are bridging the gap between advanced mathematics, computer science, and real-world public health.
Unlike traditional AI that just looks for patterns, we are on a mission to uncover true cause-and-effect relationships. By understanding the “why” behind health data, we can design better interventions, fairer systems, and safer treatments.
Here are the six key research themes driving our mission to transform health policy and patient care:
Causal Discovery from Real-World Data
Real-world health data is incredibly complex, full of feedback loops and temporal shifts. We are leveraging the latest in graph theory, machine learning, and statistics to untangle this data and map out true cause-and-effect relationships from routine health datasets.
Learning Causal Graphs from Literature
How do we make sense of thousands of published health studies? We are developing innovative AI tools to automate and scale evidence synthesis. By algorithmically mapping relationships from existing literature, we are turning vast amounts of research into clear, actionable causal maps.
Causal Impact of Nutrition Policies
Do dietary recommendations, food policies and regulations actually work? We are using graphical causal models to evaluate the real-world health impacts of nutrition policies. Our goal is to build a healthier food system and provide user-friendly tools for policymakers and the public.
Fairness in Healthcare Delivery
Everyone deserves equal access to quality care. By applying a cutting-edge Causal AI framework, we are identifying the hidden systemic forces that create unequal utilization of health services. Our insights aim to inform the changes needed to reduce disparities in underserved populations.
Causal Inference in Drug Safety
Patient safety is paramount. We are applying causal models to evaluate the safety of medications, tackling complex issues like drug interactions and dosage variability. This research will directly enhance patient care by refining medication guidelines for conditions like spinal cord injuries and Parkinson’s disease.
Personalized Healthcare Decision-Making
Healthcare isn’t one-size-fits-all. We are predicting how individual patients will respond to different treatments based on their unique genetic, lifestyle, and environmental factors. By identifying individualized treatment effects through counterfactual reasoning, we want to ensure the right treatment reaches the right person at the right time.