Frontiers in Causal Inference

Description

Causal inference plays a foundational role across the social, health, and environmental sciences, and continues to evolve as real-world applications grow more complex. This workshop will bring together researchers and practitioners advancing both methodology and applications to address challenges such as the design of complex experiments, overt and hidden bias in observational studies, data integration from diverse sources, treatment effect heterogeneity, and the use of AI to scale and enhance causal analysis. By engaging experts from statistics, biostatistics, epidemiology, economics, computer science, and related fields, the workshop will highlight recent progress, foster cross-disciplinary dialogue, and inspire new directions in the design and analysis of causal studies in modern, data-rich settings. Insights from these discussions will advance evidence-based policy making and support decision-making in health, economics, the social sciences, and beyond