The 2026 Symposium of Causal Inference in the Health Sciences is a leading international forum dedicated to advancing methods, applications, and policy impact of causal inference in health research. This interdisciplinary event brings together biostatisticians, epidemiologists, data scientists, clinicians, and public health researchers to explore how causal methods can generate more reliable evidence for improving health outcomes.

As health systems increasingly rely on complex observational data, real-world evidence, and large-scale health databases, the need for robust causal inference methods has never been greater. The symposium highlights innovative approaches that move beyond association to better understand cause-and-effect relationships in medicine, public health, and health policy.

Core Topics

• Modern Methods in Causal Inference – Propensity scores, inverse probability weighting, targeted learning, and doubly robust estimation
• Causal Machine Learning – Integrating AI with causal frameworks for personalized and population-level insights
• Real-World Data & Evidence – Using electronic health records, registries, and claims data for causal analysis
• Time-Varying Treatments & Longitudinal Data
• Causal Mediation and Mechanisms of Action
• Health Policy Evaluation & Comparative Effectiveness Research
• Equity-Focused Causal Analysis – Understanding disparities through causal frameworks

Who Should Attend

The symposium is ideal for professionals working at the intersection of statistics, data science, and health:

• Biostatisticians and epidemiologists
• Clinical and health services researchers
• Public health scientists
• Data scientists and machine learning specialists in healthcare
• Pharmaceutical and regulatory researchers
• Graduate students and academic faculty

What Participants Will Gain

Through keynote lectures, methodological workshops, applied case studies, and panel discussions, attendees will gain:

• Practical guidance on applying causal inference methods in health sciences
• Insights into emerging tools that combine machine learning and causal modeling
• Strategies for improving the validity of observational health research
• Approaches to translating causal findings into clinical and policy decisions
• Opportunities to collaborate across disciplines advancing evidence-based health research

Why This Symposium Matters

High-quality causal evidence is essential for effective clinical guidelines, public health interventions, and health policy decisions. The 2026 Symposium of Causal Inference in the Health Sciences supports the development of rigorous, transparent, and impactful research that helps decision-makers move from correlation to causation.

This event serves as a key meeting point for the global community working to strengthen the scientific foundations of causal analysis in healthcare and public health.