Seventh Seattle Symposium in Biostatistics
The field of causal inference has seen a massive expansion in recent years and is now one of the most active areas of biostatistical research. The concepts and tools developed in causal inference are intended to support practitioners in their quest for evidence on causal relationships, often critical for scientific progress. While powerful, these tools can also be easily misunderstood or misused — this has made some biostatisticians and epidemiologists apprehensive of the growing prominence of the field.
Events
Seventh Seattle Symposium in Biostatistics
by University of Washington School of Public Health
The symposium will address: 1. how causal inference can be leveraged to inform the design and enhance the analysis of observational and randomized studies, including combinations of both; 2. how causal inference has stimulated the integration of machine learning into statistical inference; 3. how causal inference provides clarity on assumptions that suffice to infer causality from different study designs and informs strategies for sensitivity analyses.